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mlx-community/Qwen2.5-Coder-1.5B-Instruct-8bit | mlx-community | "2024-09-18T22:08:06Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"code",
"codeqwen",
"chat",
"qwen",
"qwen-coder",
"mlx",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-Coder-1.5B",
"base_model:finetune:Qwen/Qwen2.5-Coder-1.5B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-09-18T22:07:49Z" | ---
base_model:
- Qwen/Qwen2.5-Coder-1.5B
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- code
- codeqwen
- chat
- qwen
- qwen-coder
- mlx
---
# mlx-community/Qwen2.5-Coder-1.5B-Instruct-8bit
The Model [mlx-community/Qwen2.5-Coder-1.5B-Instruct-8bit](https://huggingface.co/mlx-community/Qwen2.5-Coder-1.5B-Instruct-8bit) was converted to MLX format from [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) using mlx-lm version **0.18.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Qwen2.5-Coder-1.5B-Instruct-8bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
Krabat/Qwen-Qwen1.5-0.5B-1726697276 | Krabat | "2024-09-18T22:07:59Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-18T22:07:56Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
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### Framework versions
- PEFT 0.12.0 |
Echo9Zulu/Hermes-3-Llama-3.1-70B-int4-ov | Echo9Zulu | "2024-09-18T22:26:25Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-09-18T22:08:10Z" | ---
license: mit
---
|
agencia4leads/xavimartinvia | agencia4leads | "2024-09-18T22:08:32Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-18T22:08:31Z" | Entry not found |
Tomas5141/Modelo | Tomas5141 | "2024-09-18T22:09:31Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-18T22:09:30Z" | Entry not found |
mlx-community/Qwen2.5-Coder-1.5B-Instruct-4bit | mlx-community | "2024-09-18T22:11:12Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"code",
"codeqwen",
"chat",
"qwen",
"qwen-coder",
"mlx",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-Coder-1.5B",
"base_model:finetune:Qwen/Qwen2.5-Coder-1.5B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-09-18T22:10:49Z" | ---
base_model:
- Qwen/Qwen2.5-Coder-1.5B
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- code
- codeqwen
- chat
- qwen
- qwen-coder
- mlx
---
# mlx-community/Qwen2.5-Coder-1.5B-Instruct-4bit
The Model [mlx-community/Qwen2.5-Coder-1.5B-Instruct-4bit](https://huggingface.co/mlx-community/Qwen2.5-Coder-1.5B-Instruct-4bit) was converted to MLX format from [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) using mlx-lm version **0.18.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Qwen2.5-Coder-1.5B-Instruct-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
dogssss/Qwen-Qwen1.5-1.8B-1726697469 | dogssss | "2024-09-18T22:11:14Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-18T22:11:09Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
## Training Details
### Training Data
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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### Framework versions
- PEFT 0.12.0 |
tronsdds/google-gemma-7b-1726697482 | tronsdds | "2024-09-18T22:12:10Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-18T22:11:22Z" | ---
base_model: google/gemma-7b
library_name: peft
---
# Model Card for Model ID
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## Model Details
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- PEFT 0.12.0 |
SALUTEASD/Qwen-Qwen1.5-1.8B-1726697510 | SALUTEASD | "2024-09-18T22:11:54Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-18T22:11:49Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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**BibTeX:**
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### Framework versions
- PEFT 0.12.0 |
huazi123/google-gemma-2b-1726697585 | huazi123 | "2024-09-18T22:13:09Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-18T22:13:03Z" | ---
base_model: google/gemma-2b
library_name: peft
---
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<!-- Provide the basic links for the model. -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- PEFT 0.12.0 |
tronsdds/Qwen-Qwen1.5-1.8B-1726697616 | tronsdds | "2024-09-18T22:13:47Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-18T22:13:36Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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## Model Details
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
## Training Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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### Framework versions
- PEFT 0.12.0 |
BarthNotes/Eagle405 | BarthNotes | "2024-09-18T22:16:51Z" | 0 | 0 | adapter-transformers | [
"adapter-transformers",
"finance",
"question-answering",
"dataset:vidore/colpali_train_set",
"base_model:meta-llama/Meta-Llama-3.1-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3.1-8B-Instruct",
"license:apache-2.0",
"region:us"
] | question-answering | "2024-09-18T22:13:50Z" | ---
license: apache-2.0
datasets:
- vidore/colpali_train_set
metrics:
- accuracy
base_model:
- meta-llama/Meta-Llama-3.1-8B-Instruct
pipeline_tag: question-answering
library_name: adapter-transformers
tags:
- finance
--- |
sigridjineth/jina-embedding-v3-gte | sigridjineth | "2024-09-18T22:19:16Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-18T22:14:19Z" | # Running Jina Embedding V3 on Text-Embedding-Inference
* See branch: TEI-support
* Changes Made to GTE styled architecture:
1. Removed the "roberta" prefix from all tensor names.
2. Renamed "mixer" to "attention" in encoder layers.
3. Converted "Wqkv" to "qkv_proj" for combined query, key, value projections.
4. Renamed "mlp.fc1" to "mlp.up_proj" and "mlp.fc2" to "mlp.down_proj".
5. Created "mlp.up_gate_proj" by duplicating and expanding "mlp.up_proj".
6. Renamed "norm1" to "attn_ln" and "norm2" to "mlp_ln" in encoder layers.
7. Changed "emb_ln" to "embeddings.LayerNorm".
8. Renamed "weight" to "gamma" and "bias" to "beta" for layer normalization layers.
9. Removed LoRA-related tensors.
Features:
1. Structural Compatibility: The renamed model now closely matches the expected GTE architecture, allowing it to load without "tensor not found" errors.
2. Preservation of Core Weights: Most of the original model's weights are preserved, maintaining some of the learned features.
3. Adaptability: The script can handle various naming conventions and structures, making it somewhat flexible for future adjustments.
4. Transparency: The script provides a clear view of the tensor names and shapes after conversion, aiding in debugging.
Limitations:
1. Approximated Architecture: The conversion is an approximation of the GTE architecture, not an exact match. This may affect model performance.
2. Loss of LoRA Adaptations: By removing LoRA-related tensors, we've lost the fine-tuning adaptations, potentially impacting the model's specialized capabilities.
3. Up-Gate Projection Approximation: The "up_gate_proj" is created by duplicating weights, which may not accurately represent the intended GTE architecture.
4. Potential Performance Impact: The structural changes, especially in the MLP layers, may affect the model's performance and output quality.
5. Lack of Positional Embeddings Handling: We haven't specifically addressed positional embeddings, which might be different between XLM-RoBERTa and GTE models.
6. Possible Missing Specialized Layers: There might be specialized layers or components in the GTE architecture that we haven't accounted for.
7. No Guarantee of Functional Equivalence: While the model now loads, there's no guarantee it will function identically to a true GTE model.
8. Config File Mismatch: We haven't addressed potential mismatches in the config.json file, which might cause issues during model initialization or inference.
|
SALUTEASD/google-gemma-2b-1726697672 | SALUTEASD | "2024-09-18T22:16:23Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-18T22:14:31Z" | ---
base_model: google/gemma-2b
library_name: peft
---
# Model Card for Model ID
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## Model Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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tronsdds/google-gemma-2b-1726697721 | tronsdds | "2024-09-18T22:15:57Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-18T22:15:22Z" | ---
base_model: google/gemma-2b
library_name: peft
---
# Model Card for Model ID
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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dogssss/Qwen-Qwen1.5-0.5B-1726697744 | dogssss | "2024-09-18T22:15:51Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-18T22:15:45Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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huazi123/Qwen-Qwen1.5-0.5B-1726697796 | huazi123 | "2024-09-18T22:16:38Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-18T22:16:34Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
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- **Hardware Type:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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[More Information Needed]
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### Framework versions
- PEFT 0.12.0 |
tronsdds/google-gemma-7b-1726697944 | tronsdds | "2024-09-18T22:19:54Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-18T22:19:04Z" | ---
base_model: google/gemma-7b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
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### Framework versions
- PEFT 0.12.0 |
jkazdan/collapse_gemma-2-2b_hs2_replace_iter10_sftsd1 | jkazdan | "2024-09-18T22:21:46Z" | 0 | 0 | null | [
"safetensors",
"gemma2",
"trl",
"sft",
"generated_from_trainer",
"base_model:google/gemma-2-2b",
"base_model:finetune:google/gemma-2-2b",
"license:gemma",
"region:us"
] | null | "2024-09-18T22:19:09Z" | ---
license: gemma
base_model: google/gemma-2-2b
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: collapse_gemma-2-2b_hs2_replace_iter10_sftsd1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# collapse_gemma-2-2b_hs2_replace_iter10_sftsd1
This model is a fine-tuned version of [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6696
- Num Input Tokens Seen: 8069592
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-06
- train_batch_size: 8
- eval_batch_size: 16
- seed: 1
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:------:|:----:|:---------------:|:-----------------:|
| No log | 0 | 0 | 1.3956 | 0 |
| 1.6377 | 0.0316 | 5 | 1.3108 | 263072 |
| 1.1505 | 0.0632 | 10 | 1.2446 | 517208 |
| 0.7821 | 0.0947 | 15 | 1.3348 | 769888 |
| 0.5 | 0.1263 | 20 | 1.5482 | 1037144 |
| 0.2735 | 0.1579 | 25 | 1.6972 | 1289920 |
| 0.1577 | 0.1895 | 30 | 1.8590 | 1549584 |
| 0.1591 | 0.2211 | 35 | 2.0535 | 1806400 |
| 0.0789 | 0.2527 | 40 | 2.2425 | 2074288 |
| 0.0452 | 0.2842 | 45 | 2.3649 | 2329816 |
| 0.0415 | 0.3158 | 50 | 2.4586 | 2578632 |
| 0.0324 | 0.3474 | 55 | 2.5176 | 2837904 |
| 0.0304 | 0.3790 | 60 | 2.5963 | 3082160 |
| 0.0255 | 0.4106 | 65 | 2.6502 | 3339600 |
| 0.0273 | 0.4422 | 70 | 2.6701 | 3591560 |
| 0.028 | 0.4737 | 75 | 2.6985 | 3840656 |
| 0.0256 | 0.5053 | 80 | 2.6940 | 4100552 |
| 0.027 | 0.5369 | 85 | 2.6789 | 4356792 |
| 0.0266 | 0.5685 | 90 | 2.6323 | 4606856 |
| 0.0276 | 0.6001 | 95 | 2.6233 | 4858464 |
| 0.0273 | 0.6317 | 100 | 2.6164 | 5115304 |
| 0.0256 | 0.6632 | 105 | 2.6240 | 5366024 |
| 0.0266 | 0.6948 | 110 | 2.6399 | 5619944 |
| 0.0271 | 0.7264 | 115 | 2.6708 | 5867752 |
| 0.0253 | 0.7580 | 120 | 2.6610 | 6119896 |
| 0.027 | 0.7896 | 125 | 2.6729 | 6375064 |
| 0.0228 | 0.8212 | 130 | 2.6813 | 6630984 |
| 0.0245 | 0.8527 | 135 | 2.6851 | 6879904 |
| 0.0229 | 0.8843 | 140 | 2.6942 | 7128024 |
| 0.0242 | 0.9159 | 145 | 2.6830 | 7385408 |
| 0.0255 | 0.9475 | 150 | 2.6686 | 7653440 |
| 0.0241 | 0.9791 | 155 | 2.6643 | 7918000 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
mlx-community/Qwen2.5-Coder-1.5B-bf16 | mlx-community | "2024-09-18T22:19:45Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"code",
"qwen",
"qwen-coder",
"codeqwen",
"mlx",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-1.5B",
"base_model:finetune:Qwen/Qwen2.5-1.5B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-09-18T22:19:14Z" | ---
base_model:
- Qwen/Qwen2.5-1.5B
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- code
- qwen
- qwen-coder
- codeqwen
- mlx
---
# mlx-community/Qwen2.5-Coder-1.5B-bf16
The Model [mlx-community/Qwen2.5-Coder-1.5B-bf16](https://huggingface.co/mlx-community/Qwen2.5-Coder-1.5B-bf16) was converted to MLX format from [Qwen/Qwen2.5-Coder-1.5B](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B) using mlx-lm version **0.18.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Qwen2.5-Coder-1.5B-bf16")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
CharlieW02/test_model | CharlieW02 | "2024-09-18T22:20:33Z" | 0 | 0 | null | [
"dataset:HuggingFaceFV/finevideo",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:finetune:black-forest-labs/FLUX.1-dev",
"license:apache-2.0",
"region:us"
] | null | "2024-09-18T22:20:01Z" | ---
license: apache-2.0
datasets:
- HuggingFaceFV/finevideo
base_model:
- black-forest-labs/FLUX.1-dev
--- |
SALUTEASD/Qwen-Qwen1.5-0.5B-1726698015 | SALUTEASD | "2024-09-18T22:20:20Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-18T22:20:13Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
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## Model Details
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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dogssss/Qwen-Qwen1.5-1.8B-1726698055 | dogssss | "2024-09-18T22:20:59Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-18T22:20:55Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- PEFT 0.12.0 |
Krabat/Qwen-Qwen1.5-1.8B-1726698056 | Krabat | "2024-09-18T22:21:00Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-18T22:20:56Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
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## Glossary [optional]
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### Framework versions
- PEFT 0.12.0 |
mlc-ai/Qwen2.5-72B-Instruct-q4f16_1-MLC | mlc-ai | "2024-09-19T07:10:41Z" | 0 | 1 | mlc-llm | [
"mlc-llm",
"web-llm",
"base_model:Qwen/Qwen2.5-72B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-72B-Instruct",
"region:us"
] | null | "2024-09-18T22:21:01Z" | ---
library_name: mlc-llm
base_model: Qwen/Qwen2.5-72B-Instruct
tags:
- mlc-llm
- web-llm
---
# Qwen2.5-72B-Instruct-q4f16_1-MLC
This is the [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) model in MLC format `q4f16_1`.
The model can be used for projects [MLC-LLM](https://github.com/mlc-ai/mlc-llm) and [WebLLM](https://github.com/mlc-ai/web-llm).
## Example Usage
Here are some examples of using this model in MLC LLM.
Before running the examples, please install MLC LLM by following the [installation documentation](https://llm.mlc.ai/docs/install/mlc_llm.html#install-mlc-packages).
### Chat
In command line, run
```bash
mlc_llm chat HF://mlc-ai/Qwen2.5-72B-Instruct-q4f16_1-MLC
```
### REST Server
In command line, run
```bash
mlc_llm serve HF://mlc-ai/Qwen2.5-72B-Instruct-q4f16_1-MLC
```
### Python API
```python
from mlc_llm import MLCEngine
# Create engine
model = "HF://mlc-ai/Qwen2.5-72B-Instruct-q4f16_1-MLC"
engine = MLCEngine(model)
# Run chat completion in OpenAI API.
for response in engine.chat.completions.create(
messages=[{"role": "user", "content": "What is the meaning of life?"}],
model=model,
stream=True,
):
for choice in response.choices:
print(choice.delta.content, end="", flush=True)
print("\n")
engine.terminate()
```
## Documentation
For more information on MLC LLM project, please visit our [documentation](https://llm.mlc.ai/docs/) and [GitHub repo](http://github.com/mlc-ai/mlc-llm).
|
tronsdds/Qwen-Qwen1.5-1.8B-1726698080 | tronsdds | "2024-09-18T22:21:33Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-18T22:21:20Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.12.0 |
mlx-community/Qwen2.5-Coder-7B-bf16 | mlx-community | "2024-09-18T22:39:14Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"code",
"qwen",
"qwen-coder",
"codeqwen",
"mlx",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-7B",
"base_model:finetune:Qwen/Qwen2.5-7B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-09-18T22:22:16Z" | ---
base_model:
- Qwen/Qwen2.5-7B
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-7B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- code
- qwen
- qwen-coder
- codeqwen
- mlx
---
# mlx-community/Qwen2.5-Coder-7B-bf16
The Model [mlx-community/Qwen2.5-Coder-7B-bf16](https://huggingface.co/mlx-community/Qwen2.5-Coder-7B-bf16) was converted to MLX format from [Qwen/Qwen2.5-Coder-7B](https://huggingface.co/Qwen/Qwen2.5-Coder-7B) using mlx-lm version **0.18.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Qwen2.5-Coder-7B-bf16")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
Zainabsa99/CYBERLLAMA_7B_LOGANALYSIS8 | Zainabsa99 | "2024-09-18T22:22:27Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-18T22:22:27Z" | Entry not found |
mlx-community/Qwen2.5-Coder-1.5B-8bit | mlx-community | "2024-09-18T22:23:38Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"code",
"qwen",
"qwen-coder",
"codeqwen",
"mlx",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-1.5B",
"base_model:finetune:Qwen/Qwen2.5-1.5B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-09-18T22:23:17Z" | ---
base_model:
- Qwen/Qwen2.5-1.5B
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- code
- qwen
- qwen-coder
- codeqwen
- mlx
---
# mlx-community/Qwen2.5-Coder-1.5B-8bit
The Model [mlx-community/Qwen2.5-Coder-1.5B-8bit](https://huggingface.co/mlx-community/Qwen2.5-Coder-1.5B-8bit) was converted to MLX format from [Qwen/Qwen2.5-Coder-1.5B](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B) using mlx-lm version **0.18.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Qwen2.5-Coder-1.5B-8bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
huazi123/Qwen-Qwen1.5-1.8B-1726698207 | huazi123 | "2024-09-18T22:23:29Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-18T22:23:25Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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### Framework versions
- PEFT 0.12.0 |
Krabat/google-gemma-2b-1726698224 | Krabat | "2024-09-18T22:23:47Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-18T22:23:44Z" | ---
base_model: google/gemma-2b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.12.0 |
Mottzerella/Llama-3-8B-Instruct-Finance-RAG | Mottzerella | "2024-09-18T22:23:59Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-18T22:23:59Z" | Entry not found |
rana-shahroz/mistral-7b-lora-r8-gsm8k-epochs2-adapter | rana-shahroz | "2024-09-18T22:24:10Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-09-18T22:24:06Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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SALUTEASD/Qwen-Qwen1.5-1.8B-1726698293 | SALUTEASD | "2024-09-18T22:25:06Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-18T22:24:52Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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dogssss/Qwen-Qwen1.5-0.5B-1726698331 | dogssss | "2024-09-18T22:25:36Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-18T22:25:32Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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tronsdds/google-gemma-7b-1726698375 | tronsdds | "2024-09-18T22:27:04Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-18T22:26:16Z" | ---
base_model: google/gemma-7b
library_name: peft
---
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NASJesus/broken87 | NASJesus | "2024-09-18T22:26:49Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-18T22:26:48Z" | Entry not found |
Krabat/google-gemma-7b-1726698429 | Krabat | "2024-09-18T22:27:12Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-18T22:27:09Z" | ---
base_model: google/gemma-7b
library_name: peft
---
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SALUTEASD/google-gemma-2b-1726698492 | SALUTEASD | "2024-09-18T22:28:39Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-18T22:28:11Z" | ---
base_model: google/gemma-2b
library_name: peft
---
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### Framework versions
- PEFT 0.12.0 |
chenyuezhang/llama3-8b-gptq-2bit | chenyuezhang | "2024-09-18T22:40:22Z" | 0 | 0 | null | [
"safetensors",
"llama",
"2-bit",
"gptq",
"region:us"
] | null | "2024-09-18T22:29:00Z" | Entry not found |
tronsdds/google-gemma-2b-1726698582 | tronsdds | "2024-09-18T22:30:16Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-18T22:29:42Z" | ---
base_model: google/gemma-2b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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#### Testing Data
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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### Framework versions
- PEFT 0.12.0 |
morturr/Llama-2-7b-hf-yelp_reviews-2024-09-19 | morturr | "2024-09-19T21:06:01Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | "2024-09-18T22:30:28Z" | ---
base_model: meta-llama/Llama-2-7b-hf
library_name: peft
license: llama2
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-yelp_reviews-2024-09-19
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-hf-yelp_reviews-2024-09-19
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 150
### Training results
### Framework versions
- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.2.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |
dogssss/Qwen-Qwen1.5-1.8B-1726698638 | dogssss | "2024-09-18T22:30:42Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-18T22:30:38Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.12.0 |
arielSultan/speecht5_finetuned_pini_2 | arielSultan | "2024-09-18T22:30:57Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-18T22:30:57Z" | Entry not found |
SALUTEASD/Qwen-Qwen1.5-0.5B-1726698729 | SALUTEASD | "2024-09-18T22:32:18Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-18T22:32:08Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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### Direct Use
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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### Framework versions
- PEFT 0.12.0 |
Lionar7u7/Hiba_Nasri1.0 | Lionar7u7 | "2024-09-18T22:44:47Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-18T22:32:12Z" | Entry not found |
huazi123/google-gemma-2b-1726698748 | huazi123 | "2024-09-18T22:32:31Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-18T22:32:26Z" | ---
base_model: google/gemma-2b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Testing Data, Factors & Metrics
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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### Framework versions
- PEFT 0.12.0 |
tronsdds/google-gemma-7b-1726698803 | tronsdds | "2024-09-18T22:34:11Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-18T22:33:23Z" | ---
base_model: google/gemma-7b
library_name: peft
---
# Model Card for Model ID
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## Model Details
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- PEFT 0.12.0 |
dogssss/Qwen-Qwen1.5-0.5B-1726698911 | dogssss | "2024-09-18T22:35:15Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-18T22:35:11Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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SALUTEASD/Qwen-Qwen1.5-1.8B-1726698953 | SALUTEASD | "2024-09-18T22:37:19Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-18T22:35:51Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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huazi123/Qwen-Qwen1.5-0.5B-1726698959 | huazi123 | "2024-09-18T22:36:01Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-18T22:35:57Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
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mlx-community/Qwen2.5-Math-1.5B-bf16 | mlx-community | "2024-09-18T22:39:50Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mlx",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-1.5B",
"base_model:finetune:Qwen/Qwen2.5-1.5B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-09-18T22:39:26Z" | ---
base_model: Qwen/Qwen2.5-1.5B
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-Math-1.5B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- mlx
---
# mlx-community/Qwen2.5-Math-1.5B-bf16
The Model [mlx-community/Qwen2.5-Math-1.5B-bf16](https://huggingface.co/mlx-community/Qwen2.5-Math-1.5B-bf16) was converted to MLX format from [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) using mlx-lm version **0.18.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Qwen2.5-Math-1.5B-bf16")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
tronsdds/google-gemma-7b-1726699197 | tronsdds | "2024-09-18T22:40:46Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-18T22:39:57Z" | ---
base_model: google/gemma-7b
library_name: peft
---
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Krabat/Qwen-Qwen1.5-0.5B-1726699202 | Krabat | "2024-09-18T22:40:04Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-18T22:40:02Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
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[More Information Needed]
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[More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- PEFT 0.12.0 |
SALUTEASD/google-gemma-2b-1726699213 | SALUTEASD | "2024-09-18T22:40:24Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-18T22:40:12Z" | ---
base_model: google/gemma-2b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
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dogssss/Qwen-Qwen1.5-1.8B-1726699218 | dogssss | "2024-09-18T22:40:22Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-18T22:40:18Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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tronsdds/Qwen-Qwen1.5-1.8B-1726699335 | tronsdds | "2024-09-18T22:42:27Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-18T22:42:15Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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huazi123/Qwen-Qwen1.5-1.8B-1726699370 | huazi123 | "2024-09-18T22:42:52Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-18T22:42:48Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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SALUTEASD/Qwen-Qwen1.5-0.5B-1726699434 | SALUTEASD | "2024-09-18T22:44:25Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-18T22:43:53Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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tronsdds/google-gemma-2b-1726699443 | tronsdds | "2024-09-18T22:44:38Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-18T22:44:04Z" | ---
base_model: google/gemma-2b
library_name: peft
---
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.12.0 |
dogssss/Qwen-Qwen1.5-0.5B-1726699491 | dogssss | "2024-09-18T22:44:55Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-18T22:44:52Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & Metrics
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.12.0 |
dinolaw/code-of-conduct | dinolaw | "2024-09-19T08:23:31Z" | 0 | 0 | null | [
"gguf",
"license:mit",
"region:us"
] | null | "2024-09-18T22:46:31Z" | ---
license: mit
---
|
jhonalevc1995/longformer_fine | jhonalevc1995 | "2024-09-18T22:46:58Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-18T22:46:58Z" | Entry not found |
mlx-community/Qwen2.5-Math-1.5B-8bit | mlx-community | "2024-09-18T22:47:49Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mlx",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-1.5B",
"base_model:finetune:Qwen/Qwen2.5-1.5B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-09-18T22:47:29Z" | ---
base_model: Qwen/Qwen2.5-1.5B
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-Math-1.5B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- mlx
---
# mlx-community/Qwen2.5-Math-1.5B-8bit
The Model [mlx-community/Qwen2.5-Math-1.5B-8bit](https://huggingface.co/mlx-community/Qwen2.5-Math-1.5B-8bit) was converted to MLX format from [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) using mlx-lm version **0.18.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Qwen2.5-Math-1.5B-8bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
SALUTEASD/Qwen-Qwen1.5-1.8B-1726699666 | SALUTEASD | "2024-09-18T22:48:03Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-18T22:47:45Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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### Model Sources [optional]
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.12.0 |
tronsdds/google-gemma-7b-1726699668 | tronsdds | "2024-09-18T22:48:17Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-18T22:47:48Z" | ---
base_model: google/gemma-7b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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### Framework versions
- PEFT 0.12.0 |
mlx-community/Qwen2.5-32B-Instruct-8bit | mlx-community | "2024-09-18T23:42:46Z" | 0 | 0 | mlx | [
"mlx",
"safetensors",
"qwen2",
"chat",
"text-generation",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-32B",
"base_model:finetune:Qwen/Qwen2.5-32B",
"license:apache-2.0",
"region:us"
] | text-generation | "2024-09-18T22:49:12Z" | ---
base_model: Qwen/Qwen2.5-32B
language:
- en
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-32B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- chat
- mlx
---
# mlx-community/Qwen2.5-32B-Instruct-8bit
The Model [mlx-community/Qwen2.5-32B-Instruct-8bit](https://huggingface.co/mlx-community/Qwen2.5-32B-Instruct-8bit) was converted to MLX format from [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) using mlx-lm version **0.18.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Qwen2.5-32B-Instruct-8bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
jkazdan/collapse_gemma-2-2b_hs2_replace_iter10_sftsd2 | jkazdan | "2024-09-18T22:51:54Z" | 0 | 0 | null | [
"safetensors",
"gemma2",
"trl",
"sft",
"generated_from_trainer",
"base_model:google/gemma-2-2b",
"base_model:finetune:google/gemma-2-2b",
"license:gemma",
"region:us"
] | null | "2024-09-18T22:49:28Z" | ---
license: gemma
base_model: google/gemma-2-2b
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: collapse_gemma-2-2b_hs2_replace_iter10_sftsd2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# collapse_gemma-2-2b_hs2_replace_iter10_sftsd2
This model is a fine-tuned version of [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6482
- Num Input Tokens Seen: 7747456
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-06
- train_batch_size: 8
- eval_batch_size: 16
- seed: 2
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:------:|:----:|:---------------:|:-----------------:|
| No log | 0 | 0 | 1.3956 | 0 |
| 1.6727 | 0.0315 | 5 | 1.3102 | 246184 |
| 1.009 | 0.0630 | 10 | 1.2543 | 489448 |
| 0.667 | 0.0945 | 15 | 1.3838 | 726824 |
| 0.4294 | 0.1259 | 20 | 1.5708 | 970024 |
| 0.2248 | 0.1574 | 25 | 1.6675 | 1212248 |
| 0.1908 | 0.1889 | 30 | 1.8604 | 1467208 |
| 0.1451 | 0.2204 | 35 | 2.0014 | 1710152 |
| 0.0621 | 0.2519 | 40 | 2.1971 | 1954192 |
| 0.0381 | 0.2834 | 45 | 2.3063 | 2191576 |
| 0.0367 | 0.3148 | 50 | 2.3949 | 2433592 |
| 0.036 | 0.3463 | 55 | 2.4774 | 2679696 |
| 0.0294 | 0.3778 | 60 | 2.5588 | 2928552 |
| 0.0283 | 0.4093 | 65 | 2.5792 | 3173464 |
| 0.0285 | 0.4408 | 70 | 2.6130 | 3413776 |
| 0.0246 | 0.4723 | 75 | 2.6031 | 3659144 |
| 0.0239 | 0.5037 | 80 | 2.6188 | 3912088 |
| 0.023 | 0.5352 | 85 | 2.6231 | 4148400 |
| 0.0251 | 0.5667 | 90 | 2.5840 | 4398984 |
| 0.0236 | 0.5982 | 95 | 2.5662 | 4651040 |
| 0.0264 | 0.6297 | 100 | 2.5629 | 4894920 |
| 0.0243 | 0.6612 | 105 | 2.5727 | 5137152 |
| 0.0256 | 0.6926 | 110 | 2.5955 | 5378304 |
| 0.0235 | 0.7241 | 115 | 2.6078 | 5624672 |
| 0.0242 | 0.7556 | 120 | 2.6111 | 5877704 |
| 0.024 | 0.7871 | 125 | 2.6151 | 6124640 |
| 0.0265 | 0.8186 | 130 | 2.6286 | 6367576 |
| 0.0224 | 0.8501 | 135 | 2.6392 | 6614328 |
| 0.0242 | 0.8815 | 140 | 2.6356 | 6856504 |
| 0.023 | 0.9130 | 145 | 2.6439 | 7105832 |
| 0.0238 | 0.9445 | 150 | 2.6567 | 7354200 |
| 0.0244 | 0.9760 | 155 | 2.6456 | 7601504 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
tronsdds/Qwen-Qwen1.5-1.8B-1726699783 | tronsdds | "2024-09-18T22:49:57Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-18T22:49:44Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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dogssss/Qwen-Qwen1.5-1.8B-1726699799 | dogssss | "2024-09-18T22:50:03Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-18T22:49:59Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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SALUTEASD/google-gemma-2b-1726699857 | SALUTEASD | "2024-09-18T22:50:56Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-18T22:50:56Z" | Entry not found |
tronsdds/google-gemma-2b-1726699892 | tronsdds | "2024-09-18T22:52:07Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-18T22:51:33Z" | ---
base_model: google/gemma-2b
library_name: peft
---
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huazi123/google-gemma-2b-1726699908 | huazi123 | "2024-09-18T22:51:51Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-18T22:51:46Z" | ---
base_model: google/gemma-2b
library_name: peft
---
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Maghoumi/llm-wizard | Maghoumi | "2024-09-18T23:00:03Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-09-18T22:52:21Z" | ---
library_name: transformers
tags: []
---
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Krabat/Qwen-Qwen1.5-1.8B-1726699977 | Krabat | "2024-09-18T22:52:59Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-18T22:52:57Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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megagarra/spiderman | megagarra | "2024-09-18T22:55:27Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-18T22:54:16Z" | Entry not found |
dogssss/Qwen-Qwen1.5-0.5B-1726700071 | dogssss | "2024-09-18T22:54:35Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-18T22:54:31Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
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tronsdds/google-gemma-7b-1726700115 | tronsdds | "2024-09-18T22:56:04Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-18T22:55:15Z" | ---
base_model: google/gemma-7b
library_name: peft
---
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huazi123/Qwen-Qwen1.5-0.5B-1726700119 | huazi123 | "2024-09-18T22:55:21Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-18T22:55:17Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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Krabat/google-gemma-2b-1726700145 | Krabat | "2024-09-18T22:55:51Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-18T22:55:46Z" | ---
base_model: google/gemma-2b
library_name: peft
---
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SALUTEASD/Qwen-Qwen1.5-0.5B-1726700155 | SALUTEASD | "2024-09-18T22:56:04Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-18T22:55:54Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- PEFT 0.12.0 |
JinglesDados/Tiringa | JinglesDados | "2024-09-18T22:56:52Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-09-18T22:56:14Z" | ---
license: openrail
---
|
disi-unibo-nlp/mistral-SFT-medqa-triples-nographeval-cot | disi-unibo-nlp | "2024-09-18T23:01:30Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-09-18T22:56:38Z" | ---
base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Uploaded model
- **Developed by:** disi-unibo-nlp
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.3-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
tronsdds/Qwen-Qwen1.5-1.8B-1726700252 | tronsdds | "2024-09-18T22:57:45Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-18T22:57:32Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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- PEFT 0.12.0 |
utahnlp/naturalquestionsShort_facebook_opt-6.7b_seed-1 | utahnlp | "2024-09-18T23:03:07Z" | 0 | 0 | null | [
"safetensors",
"opt",
"region:us"
] | null | "2024-09-18T22:57:47Z" | Entry not found |
Krabat/google-gemma-7b-1726700353 | Krabat | "2024-09-18T22:59:17Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-18T22:59:14Z" | ---
base_model: google/gemma-7b
library_name: peft
---
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SALUTEASD/Qwen-Qwen1.5-1.8B-1726700360 | SALUTEASD | "2024-09-18T22:59:24Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-18T22:59:19Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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tronsdds/google-gemma-2b-1726700360 | tronsdds | "2024-09-18T22:59:55Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-18T22:59:20Z" | ---
base_model: google/gemma-2b
library_name: peft
---
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SuperMari/SuperMariSeries | SuperMari | "2024-09-18T23:03:01Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-18T22:59:33Z" | Entry not found |
dogssss/Qwen-Qwen1.5-1.8B-1726700378 | dogssss | "2024-09-18T22:59:42Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-18T22:59:39Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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- PEFT 0.12.0 |
mlx-community/Qwen2.5-Math-1.5B-4bit | mlx-community | "2024-09-18T23:00:12Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mlx",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-1.5B",
"base_model:finetune:Qwen/Qwen2.5-1.5B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-09-18T22:59:54Z" | ---
base_model: Qwen/Qwen2.5-1.5B
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-Math-1.5B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- mlx
---
# mlx-community/Qwen2.5-Math-1.5B-4bit
The Model [mlx-community/Qwen2.5-Math-1.5B-4bit](https://huggingface.co/mlx-community/Qwen2.5-Math-1.5B-4bit) was converted to MLX format from [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) using mlx-lm version **0.18.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Qwen2.5-Math-1.5B-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
QuantumZ/unet | QuantumZ | "2024-09-18T23:01:50Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-18T23:01:50Z" | Entry not found |
SALUTEASD/google-gemma-2b-1726700524 | SALUTEASD | "2024-09-18T23:03:06Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-18T23:02:02Z" | ---
base_model: google/gemma-2b
library_name: peft
---
# Model Card for Model ID
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huazi123/Qwen-Qwen1.5-1.8B-1726700529 | huazi123 | "2024-09-18T23:02:11Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-18T23:02:07Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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noxneural/ChainMaker | noxneural | "2024-09-18T23:55:34Z" | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | "2024-09-18T23:03:00Z" | Entry not found |
tronsdds/google-gemma-7b-1726700583 | tronsdds | "2024-09-18T23:03:52Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-18T23:03:03Z" | ---
base_model: google/gemma-7b
library_name: peft
---
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dogssss/Qwen-Qwen1.5-0.5B-1726700657 | dogssss | "2024-09-18T23:04:21Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-18T23:04:17Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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mlx-community/Qwen2.5-Math-1.5B-Instruct-bf16 | mlx-community | "2024-09-18T23:05:20Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"mlx",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-Math-1.5B",
"base_model:finetune:Qwen/Qwen2.5-Math-1.5B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-09-18T23:04:52Z" | ---
base_model: Qwen/Qwen2.5-Math-1.5B
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-Math-1.5B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- chat
- mlx
---
# mlx-community/Qwen2.5-Math-1.5B-Instruct-bf16
The Model [mlx-community/Qwen2.5-Math-1.5B-Instruct-bf16](https://huggingface.co/mlx-community/Qwen2.5-Math-1.5B-Instruct-bf16) was converted to MLX format from [Qwen/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B-Instruct) using mlx-lm version **0.18.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Qwen2.5-Math-1.5B-Instruct-bf16")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
tronsdds/Qwen-Qwen1.5-1.8B-1726700719 | tronsdds | "2024-09-18T23:05:32Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-18T23:05:20Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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novalalthoff/wav2vec2-large-id-google-fleurs-50 | novalalthoff | "2024-09-18T23:06:55Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-09-18T23:05:27Z" | ---
library_name: transformers
tags: []
---
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### Downstream Use [optional]
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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#### Metrics
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### Results
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#### Summary
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Technical Specifications [optional]
### Model Architecture and Objective
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## Citation [optional]
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## Glossary [optional]
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