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SALUTEASD/Qwen-Qwen1.5-0.5B-1726705407 | SALUTEASD | "2024-09-19T00:23:33Z" | 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 | "2024-09-19T00:23:26Z" | ---
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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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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### Framework versions
- PEFT 0.12.0 |
happyme531/Stable-Diffusion-1.5-LCM-ONNX-RKNN2 | happyme531 | "2024-09-19T00:38:29Z" | 0 | 0 | null | [
"onnx",
"rknn",
"LCM",
"stable-diffusion",
"base_model:TheyCallMeHex/LCM-Dreamshaper-V7-ONNX",
"base_model:quantized:TheyCallMeHex/LCM-Dreamshaper-V7-ONNX",
"region:us"
] | null | "2024-09-19T00:23:30Z" | ---
base_model:
- TheyCallMeHex/LCM-Dreamshaper-V7-ONNX
tags:
- rknn
- LCM
- stable-diffusion
---
# Stable Diffusion 1.5 Latent Consistency Model for RKNN2
## (English README see below)
使用RKNPU2运行Stable Diffusion 1.5 LCM 图像生成模型！！
- 推理速度(RK3588): 单NPU核, 384x384分辨率, 4次迭代, 生成1张图片平均耗时约13.8秒
- 内存占用: 约5.2GB
## 使用方法
### 1. 克隆或者下载此仓库到本地
### 2. 安装依赖
```bash
pip install diffusers pillow numpy<2
```
当然你还要安装rknn-toolkit2-lite2。
### 3. 运行
```bash
python ./run_rknn-lcm.py -i ./model -o ./images --num-inference-steps 4 -s 384x384 --prompt "Majestic mountain landscape with snow-capped peaks, autumn foliage in vibrant reds and oranges, a turquoise river winding through a valley, crisp and serene atmosphere, ultra-realistic style."
```
## 模型转换
### 1. 下载模型
下载一个onnx格式的Stable Diffusion 1.5 LCM模型，并放到`./model`目录下。
```bash
huggingface-cli download TheyCallMeHex/LCM-Dreamshaper-V7-ONNX
cp -r -L ~/.cache/huggingface/hub/models--TheyCallMeHex--LCM-Dreamshaper-V7-ONNX/snapshots/4029a217f9cdc0437f395738d3ab686bb910ceea ./model
```
理论上你也可以通过将LCM Lora合并到普通的Stable Diffusion 1.5模型，然后转换为onnx格式，来实现LCM的推理。但是我这边也不知道怎么做，有知道的小伙伴可以提个PR。
### 2. 转换模型
```bash
# 转换模型, 384x384分辨率
python ./convert-onnx-to-rknn.py -m ./model -r 384x384
```
注意分辨率越高，模型越大，转换时间越长。不建议使用太大的分辨率。
## 已知问题
1. 截至目前，使用最新版本的rknn-toolkit2 2.2.0版本转换的模型仍然存在极其严重的精度损失！即使使用的是fp16数据类型。如图，上方是使用onnx模型推理的结果，下方是使用rknn模型推理的结果。所有参数均一致。并且分辨率越高，精度损失越严重。这是rknn-toolkit2的bug。
- 384x384:
- 256x256:
2. 其实模型转换脚本可以选择多个分辨率(例如"384x384,256x256"), 但这会导致模型转换失败。这是rknn-toolkit2的bug。
## 参考
- [TheyCallMeHex/LCM-Dreamshaper-V7-ONNX](https://huggingface.co/TheyCallMeHex/LCM-Dreamshaper-V7-ONNX)
- [Optimum's LatentConsistencyPipeline](https://github.com/huggingface/optimum/blob/main/optimum/pipelines/diffusers/pipeline_latent_consistency.py)
- [happyme531/RK3588-stable-diffusion-GPU](https://github.com/happyme531/RK3588-stable-diffusion-GPU)
## English README
# Stable Diffusion 1.5 Latent Consistency Model for RKNN2
Run the Stable Diffusion 1.5 LCM image generation model using RKNPU2!
- Inference speed (RK3588): Single NPU core, 384x384 resolution, 4 iterations, average time to generate 1 image is about 13.8 seconds
- Memory usage: About 5.2GB
## Usage
### 1. Clone or download this repository to your local machine
### 2. Install dependencies
```bash
pip install diffusers pillow numpy<2
```
Of course, you also need to install rknn-toolkit2-lite2.
### 3. Run
```bash
python ./run_rknn-lcm.py -i ./model -o ./images --num-inference-steps 4 -s 384x384 --prompt "Majestic mountain landscape with snow-capped peaks, autumn foliage in vibrant reds and oranges, a turquoise river winding through a valley, crisp and serene atmosphere, ultra-realistic style."
```
## Model Conversion
### 1. Download the model
Download a Stable Diffusion 1.5 LCM model in ONNX format and place it in the `./model` directory.
```bash
huggingface-cli download TheyCallMeHex/LCM-Dreamshaper-V7-ONNX
cp -r -L ~/.cache/huggingface/hub/models--TheyCallMeHex--LCM-Dreamshaper-V7-ONNX/snapshots/4029a217f9cdc0437f395738d3ab686bb910ceea ./model
```
In theory, you could also achieve LCM inference by merging the LCM Lora into a regular Stable Diffusion 1.5 model and then converting it to ONNX format. However, I'm not sure how to do this. If anyone knows, please feel free to submit a PR.
### 2. Convert the model
```bash
# Convert the model, 384x384 resolution
python ./convert-onnx-to-rknn.py -m ./model -r 384x384
```
Note that the higher the resolution, the larger the model and the longer the conversion time. It's not recommended to use very high resolutions.
## Known Issues
1. As of now, models converted using the latest version of rknn-toolkit2 (version 2.2.0) still suffer from severe precision loss, even when using fp16 data type. As shown in the image, the top is the result of inference using the ONNX model, and the bottom is the result using the RKNN model. All parameters are the same. Moreover, the higher the resolution, the more severe the precision loss. This is a bug in rknn-toolkit2.
- 384x384:
- 256x256:
2. Actually, the model conversion script can select multiple resolutions (e.g., "384x384,256x256"), but this causes the model conversion to fail. This is a bug in rknn-toolkit2.
## References
- [TheyCallMeHex/LCM-Dreamshaper-V7-ONNX](https://huggingface.co/TheyCallMeHex/LCM-Dreamshaper-V7-ONNX)
- [Optimum's LatentConsistencyPipeline](https://github.com/huggingface/optimum/blob/main/optimum/pipelines/diffusers/pipeline_latent_consistency.py)
- [happyme531/RK3588-stable-diffusion-GPU](https://github.com/happyme531/RK3588-stable-diffusion-GPU) |
7siwek/llama381binstruct_summarize_short_merged | 7siwek | "2024-09-19T00:27:04Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2024-09-19T00:23:34Z" | ---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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amonig/dippy_6020847005 | amonig | "2024-09-19T00:28:19Z" | 0 | 0 | null | [
"safetensors",
"mistral",
"region:us"
] | null | "2024-09-19T00:24:26Z" | Entry not found |
tronsdds/google-gemma-2b-1726705514 | tronsdds | "2024-09-19T00:25:49Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-19T00:25:14Z" | ---
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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dogssss/Qwen-Qwen1.5-1.8B-1726705619 | dogssss | "2024-09-19T00:27:03Z" | 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 | "2024-09-19T00:27:00Z" | ---
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. -->
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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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SALUTEASD/Qwen-Qwen1.5-1.8B-1726705624 | SALUTEASD | "2024-09-19T00:27:13Z" | 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 | "2024-09-19T00:27:03Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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Krabat/Qwen-Qwen1.5-1.8B-1726705705 | Krabat | "2024-09-19T00:28:27Z" | 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 | "2024-09-19T00:28:25Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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huazi123/google-gemma-2b-1726705721 | huazi123 | "2024-09-19T00:29:26Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-19T00:28:38Z" | ---
base_model: google/gemma-2b
library_name: peft
---
# Model Card for Model ID
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## Model Details
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tronsdds/google-gemma-7b-1726705738 | tronsdds | "2024-09-19T00:29:47Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-19T00:28:58Z" | ---
base_model: google/gemma-7b
library_name: peft
---
# Model Card for Model ID
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## Model Details
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### Framework versions
- PEFT 0.12.0 |
randiapoorva/ar | randiapoorva | "2024-09-19T01:23:05Z" | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2024-09-19T00:29:32Z" | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: Amrita Roy
---
# Ar
<!-- <Gallery /> -->
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `Amrita Roy` to trigger the image generation.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('randiapoorva/ar', weight_name='lora.safetensors')
image = pipeline('your prompt').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
sakshamdura/ppo-SnowballTarget | sakshamdura | "2024-09-19T00:29:45Z" | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | "2024-09-19T00:29:43Z" | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: sakshamdura/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
SALUTEASD/google-gemma-2b-1726705807 | SALUTEASD | "2024-09-19T00:30:15Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-19T00:30:06Z" | ---
base_model: google/gemma-2b
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
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[More Information Needed]
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- PEFT 0.12.0 |
baseten/tllama-brit-spec-dec-v1 | baseten | "2024-09-19T00:31:11Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-19T00:30:41Z" | Entry not found |
Krabat/google-gemma-2b-1726705870 | Krabat | "2024-09-19T00:31:14Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-19T00:31:10Z" | ---
base_model: google/gemma-2b
library_name: peft
---
# Model Card for Model ID
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tronsdds/Qwen-Qwen1.5-1.8B-1726705871 | tronsdds | "2024-09-19T00:31:24Z" | 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 | "2024-09-19T00:31:12Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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- PEFT 0.12.0 |
dogssss/Qwen-Qwen1.5-0.5B-1726705892 | dogssss | "2024-09-19T00:31:37Z" | 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 | "2024-09-19T00:31:32Z" | ---
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.
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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 |
utahnlp/newsqa_gpt2_seed-1 | utahnlp | "2024-09-19T00:32:12Z" | 0 | 0 | null | [
"safetensors",
"gpt2",
"region:us"
] | null | "2024-09-19T00:31:50Z" | Entry not found |
utahnlp/newsqa_gpt2_seed-2 | utahnlp | "2024-09-19T00:32:41Z" | 0 | 0 | null | [
"safetensors",
"gpt2",
"region:us"
] | null | "2024-09-19T00:32:16Z" | Entry not found |
ccibeekeoc42/Llama3.1-8b-instruct-SFT-2024-09-19_LoRAs | ccibeekeoc42 | "2024-09-19T15:44:02Z" | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3.1-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3.1-8B-Instruct",
"license:llama3.1",
"region:us"
] | null | "2024-09-19T00:32:42Z" | ---
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
library_name: peft
license: llama3.1
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama3.1-8b-instruct-SFT-2024-09-19_LoRAs
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. -->
# Llama3.1-8b-instruct-SFT-2024-09-19_LoRAs
This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3202
## 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: 1e-05
- train_batch_size: 6
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 1.5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 1.6086 | 0.0795 | 1000 | 1.6472 |
| 1.4132 | 0.1589 | 2000 | 1.5850 |
| 1.3613 | 0.2384 | 3000 | 1.5402 |
| 1.3118 | 0.3178 | 4000 | 1.5102 |
| 1.3045 | 0.3973 | 5000 | 1.4901 |
| 1.2856 | 0.4767 | 6000 | 1.4674 |
| 1.2646 | 0.5562 | 7000 | 1.4431 |
| 1.2471 | 0.6356 | 8000 | 1.4282 |
| 1.2497 | 0.7151 | 9000 | 1.4089 |
| 1.2171 | 0.7945 | 10000 | 1.4051 |
| 1.2145 | 0.8740 | 11000 | 1.3926 |
| 1.2103 | 0.9534 | 12000 | 1.3849 |
| 1.1813 | 1.0329 | 13000 | 1.3707 |
| 1.1696 | 1.1123 | 14000 | 1.3620 |
| 1.1459 | 1.1918 | 15000 | 1.3536 |
| 1.1486 | 1.2713 | 16000 | 1.3413 |
| 1.1398 | 1.3507 | 17000 | 1.3324 |
| 1.1322 | 1.4302 | 18000 | 1.3202 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.0.1+cu118
- Datasets 3.0.0
- Tokenizers 0.19.1 |
utahnlp/newsqa_gpt2_seed-3 | utahnlp | "2024-09-19T00:33:15Z" | 0 | 0 | null | [
"safetensors",
"gpt2",
"region:us"
] | null | "2024-09-19T00:32:46Z" | Entry not found |
huazi123/Qwen-Qwen1.5-0.5B-1726705980 | huazi123 | "2024-09-19T00:33:00Z" | 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 | "2024-09-19T00:32:57Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
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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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## Model Examination [optional]
<!-- 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]
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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 |
tronsdds/google-gemma-2b-1726705977 | tronsdds | "2024-09-19T00:33:31Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-19T00:32:57Z" | ---
base_model: google/gemma-2b
library_name: peft
---
# Model Card for Model ID
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## Model Details
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utahnlp/newsqa_gpt2-medium_seed-1 | utahnlp | "2024-09-19T00:34:22Z" | 0 | 0 | null | [
"safetensors",
"gpt2",
"region:us"
] | null | "2024-09-19T00:33:21Z" | Entry not found |
utahnlp/newsqa_gpt2-medium_seed-2 | utahnlp | "2024-09-19T00:35:28Z" | 0 | 0 | null | [
"safetensors",
"gpt2",
"region:us"
] | null | "2024-09-19T00:34:29Z" | Entry not found |
SALUTEASD/Qwen-Qwen1.5-0.5B-1726706075 | SALUTEASD | "2024-09-19T00:34:44Z" | 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 | "2024-09-19T00:34:34Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
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Krabat/google-gemma-7b-1726706077 | Krabat | "2024-09-19T00:34:40Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-19T00:34:37Z" | ---
base_model: google/gemma-7b
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 |
TomShales123/edge-maxxing-1-h9 | TomShales123 | "2024-09-19T01:09:21Z" | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | "2024-09-19T00:34:56Z" | Entry not found |
amonig/dippy_2286661505 | amonig | "2024-09-19T00:38:26Z" | 0 | 0 | null | [
"safetensors",
"llama",
"region:us"
] | null | "2024-09-19T00:35:19Z" | Entry not found |
utahnlp/newsqa_gpt2-medium_seed-3 | utahnlp | "2024-09-19T00:36:32Z" | 0 | 0 | null | [
"safetensors",
"gpt2",
"region:us"
] | null | "2024-09-19T00:35:34Z" | Entry not found |
Thelocallab/Amateur | Thelocallab | "2024-09-19T00:54:02Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-09-19T00:36:15Z" | ---
license: apache-2.0
---
|
dogssss/Qwen-Qwen1.5-1.8B-1726706199 | dogssss | "2024-09-19T00:36:43Z" | 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 | "2024-09-19T00:36:39Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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tronsdds/google-gemma-7b-1726706200 | tronsdds | "2024-09-19T00:37:28Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-19T00:36:40Z" | ---
base_model: google/gemma-7b
library_name: peft
---
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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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utahnlp/newsqa_gpt2-large_seed-1 | utahnlp | "2024-09-19T00:38:48Z" | 0 | 0 | null | [
"safetensors",
"gpt2",
"region:us"
] | null | "2024-09-19T00:36:44Z" | Entry not found |
SALUTEASD/Qwen-Qwen1.5-1.8B-1726706293 | SALUTEASD | "2024-09-19T00:38:18Z" | 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 | "2024-09-19T00:38:12Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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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
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
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#### Preprocessing [optional]
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## 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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- **Compute Region:** [More Information Needed]
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## Technical Specifications [optional]
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### Framework versions
- PEFT 0.12.0 |
maartenx01/ppo-Pyramids | maartenx01 | "2024-09-19T00:38:41Z" | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | "2024-09-19T00:38:39Z" | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: maartenx01/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
tronsdds/Qwen-Qwen1.5-1.8B-1726706338 | tronsdds | "2024-09-19T00:39:11Z" | 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 | "2024-09-19T00:38:58Z" | ---
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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## Bias, Risks, and Limitations
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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.
## 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).
- **Hardware Type:** [More Information Needed]
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- PEFT 0.12.0 |
utahnlp/newsqa_gpt2-large_seed-2 | utahnlp | "2024-09-19T00:40:56Z" | 0 | 0 | null | [
"safetensors",
"gpt2",
"region:us"
] | null | "2024-09-19T00:39:00Z" | Entry not found |
jerseyjerry/google-gemma-2b-it-1726706359 | jerseyjerry | "2024-09-19T00:39:30Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b-it",
"base_model:adapter:google/gemma-2b-it",
"region:us"
] | null | "2024-09-19T00:39:19Z" | ---
base_model: google/gemma-2b-it
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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- **Developed by:** [More Information Needed]
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[More Information Needed]
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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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[More Information Needed]
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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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[More Information Needed]
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huazi123/Qwen-Qwen1.5-1.8B-1726706392 | huazi123 | "2024-09-19T00:39:50Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-19T00:39:50Z" | Entry not found |
tronsdds/google-gemma-2b-1726706448 | tronsdds | "2024-09-19T00:41:24Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-19T00:40:49Z" | ---
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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- **Developed by:** [More Information Needed]
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[More Information Needed]
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
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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
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## 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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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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- PEFT 0.12.0 |
utahnlp/newsqa_gpt2-large_seed-3 | utahnlp | "2024-09-19T00:43:11Z" | 0 | 0 | null | [
"safetensors",
"gpt2",
"region:us"
] | null | "2024-09-19T00:41:08Z" | Entry not found |
dogssss/Qwen-Qwen1.5-0.5B-1726706472 | dogssss | "2024-09-19T00:41:16Z" | 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 | "2024-09-19T00:41:12Z" | ---
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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- **Developed by:** [More Information Needed]
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[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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<!-- 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]
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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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## 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]
## 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 |
lexu7355/lora-mary | lexu7355 | "2024-09-19T00:41:12Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-19T00:41:12Z" | Entry not found |
Christine59195/Augustin | Christine59195 | "2024-09-19T01:24:01Z" | 0 | 0 | null | [
"license:other",
"region:us"
] | null | "2024-09-19T00:43:20Z" | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
--- |
utahnlp/newsqa_gpt2-xl_seed-1 | utahnlp | "2024-09-19T00:46:33Z" | 0 | 0 | null | [
"safetensors",
"gpt2",
"region:us"
] | null | "2024-09-19T00:43:35Z" | Entry not found |
Thelocallab/Cyber | Thelocallab | "2024-09-19T00:45:38Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-09-19T00:43:53Z" | ---
license: apache-2.0
---
|
cfiscko/dqn-SpaceInvadersNoFrameskip-v4 | cfiscko | "2024-09-19T00:45:03Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2024-09-19T00:44:31Z" | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 450.50 +/- 213.43
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga cfiscko -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga cfiscko -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga cfiscko
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
tronsdds/google-gemma-7b-1726706673 | tronsdds | "2024-09-19T00:45:21Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-19T00:44:33Z" | ---
base_model: google/gemma-7b
library_name: peft
---
# Model Card for Model ID
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SALUTEASD/Qwen-Qwen1.5-0.5B-1726706681 | SALUTEASD | "2024-09-19T00:44:53Z" | 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 | "2024-09-19T00:44:40Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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## Model Details
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i5clj/lora_model | i5clj | "2024-09-19T01:21:38Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-09-19T00:46:02Z" | ---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** i5clj
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-bnb-4bit
This llama 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)
|
yunhuijang/vfh6kkak | yunhuijang | "2024-09-19T00:47:52Z" | 0 | 0 | null | [
"safetensors",
"t5",
"region:us"
] | null | "2024-09-19T00:46:08Z" | Entry not found |
dogssss/Qwen-Qwen1.5-1.8B-1726706776 | dogssss | "2024-09-19T00:46:20Z" | 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 | "2024-09-19T00:46:17Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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tronsdds/Qwen-Qwen1.5-1.8B-1726706807 | tronsdds | "2024-09-19T00:46:59Z" | 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 | "2024-09-19T00:46:47Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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utahnlp/newsqa_gpt2-xl_seed-2 | utahnlp | "2024-09-19T00:50:01Z" | 0 | 0 | null | [
"safetensors",
"gpt2",
"region:us"
] | null | "2024-09-19T00:46:54Z" | Entry not found |
Krabat/Qwen-Qwen1.5-0.5B-1726706852 | Krabat | "2024-09-19T00:47:34Z" | 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 | "2024-09-19T00:47:32Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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tronsdds/google-gemma-2b-1726706916 | tronsdds | "2024-09-19T00:49:10Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-19T00:48:36Z" | ---
base_model: google/gemma-2b
library_name: peft
---
# Model Card for Model ID
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wentao-yuan/where2place | wentao-yuan | "2024-09-19T00:48:38Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-19T00:48:36Z" | Entry not found |
huazi123/google-gemma-2b-1726706945 | huazi123 | "2024-09-19T00:49:08Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-19T00:49:03Z" | ---
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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- PEFT 0.12.0 |
SALUTEASD/Qwen-Qwen1.5-1.8B-1726707021 | SALUTEASD | "2024-09-19T00:50:20Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-19T00:50:20Z" | Entry not found |
utahnlp/newsqa_gpt2-xl_seed-3 | utahnlp | "2024-09-19T00:53:23Z" | 0 | 0 | null | [
"safetensors",
"gpt2",
"region:us"
] | null | "2024-09-19T00:50:23Z" | Entry not found |
SBYYB/gpt2-MEDICAL-SHORT-MED-50000 | SBYYB | "2024-09-19T02:01:22Z" | 0 | 0 | null | [
"tensorboard",
"safetensors",
"gpt2",
"region:us"
] | null | "2024-09-19T00:50:25Z" | Entry not found |
dogssss/Qwen-Qwen1.5-0.5B-1726707050 | dogssss | "2024-09-19T00:50:54Z" | 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 | "2024-09-19T00:50:51Z" | ---
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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sting01/Qwen-Qwen1.5-1.8B-1726707060 | sting01 | "2024-09-19T00:51:06Z" | 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 | "2024-09-19T00:51:00Z" | ---
base_model: Qwen/Qwen1.5-1.8B
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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tronsdds/google-gemma-7b-1726707138 | tronsdds | "2024-09-19T00:53:30Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-19T00:52:18Z" | ---
base_model: google/gemma-7b
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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huazi123/Qwen-Qwen1.5-0.5B-1726707151 | huazi123 | "2024-09-19T00:52:32Z" | 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 | "2024-09-19T00:52:29Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
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saxon/multillava-next-vicuna7b-siglip-finetune1.5multiloss-lfr1e-3-45000 | saxon | "2024-09-19T01:02:27Z" | 0 | 0 | null | [
"safetensors",
"llama",
"region:us"
] | null | "2024-09-19T00:53:03Z" | Entry not found |
utahnlp/newsqa_t5-small_seed-1 | utahnlp | "2024-09-19T00:53:43Z" | 0 | 0 | null | [
"safetensors",
"t5",
"region:us"
] | null | "2024-09-19T00:53:28Z" | Entry not found |
rana-shahroz/mistral-7b-openorca-lora-r8-winogrande-epochs3-adapter | rana-shahroz | "2024-09-19T00:53:37Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-09-19T00:53:33Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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utahnlp/newsqa_t5-small_seed-2 | utahnlp | "2024-09-19T00:53:55Z" | 0 | 0 | null | [
"safetensors",
"t5",
"region:us"
] | null | "2024-09-19T00:53:45Z" | Entry not found |
utahnlp/newsqa_t5-small_seed-3 | utahnlp | "2024-09-19T00:54:08Z" | 0 | 0 | null | [
"safetensors",
"t5",
"region:us"
] | null | "2024-09-19T00:53:58Z" | Entry not found |
utahnlp/newsqa_t5-base_seed-1 | utahnlp | "2024-09-19T00:54:45Z" | 0 | 0 | null | [
"safetensors",
"t5",
"region:us"
] | null | "2024-09-19T00:54:13Z" | Entry not found |
Orion-zhen/Qwen2.5-32B-Instruct-6.5bpw-exl2 | Orion-zhen | "2024-09-19T06:20:48Z" | 0 | 0 | null | [
"safetensors",
"qwen2",
"chat",
"text-generation",
"conversational",
"en",
"arxiv:2309.00071",
"base_model:Qwen/Qwen2.5-32B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-32B-Instruct",
"license:apache-2.0",
"exl2",
"region:us"
] | text-generation | "2024-09-19T00:54:37Z" | ---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-32B-Instruct/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
base_model:
- Qwen/Qwen2.5-32B-Instruct
tags:
- chat
---
# Qwen2.5-32B-Instruct
## Introduction
Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
- Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains.
- Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots.
- **Long-context Support** up to 128K tokens and can generate up to 8K tokens.
- **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
**This repo contains the instruction-tuned 32B Qwen2.5 model**, which has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
- Number of Parameters: 32.5B
- Number of Paramaters (Non-Embedding): 31.0B
- Number of Layers: 64
- Number of Attention Heads (GQA): 40 for Q and 8 for KV
- Context Length: Full 131,072 tokens and generation 8192 tokens
- Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts.
For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Requirements
The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.37.0`, you will encounter the following error:
```
KeyError: 'qwen2'
```
## Quickstart
Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen2.5-32B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
### Processing Long Texts
The current `config.json` is set for context length up to 32,768 tokens.
To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
For supported frameworks, you could add the following to `config.json` to enable YaRN:
```json
{
...,
"rope_scaling": {
"factor": 4.0,
"original_max_position_embeddings": 32768,
"type": "yarn"
}
}
```
For deployment, we recommend using vLLM.
Please refer to our [Documentation](https://qwen.readthedocs.io/en/latest/deployment/vllm.html) for usage if you are not familar with vLLM.
Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts**.
We advise adding the `rope_scaling` configuration only when processing long contexts is required.
## Evaluation & Performance
Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/).
For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
## Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen2.5,
title = {Qwen2.5: A Party of Foundation Models},
url = {https://qwenlm.github.io/blog/qwen2.5/},
author = {Qwen Team},
month = {September},
year = {2024}
}
@article{qwen2,
title={Qwen2 Technical Report},
author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
journal={arXiv preprint arXiv:2407.10671},
year={2024}
}
``` |
utahnlp/newsqa_t5-base_seed-2 | utahnlp | "2024-09-19T00:55:24Z" | 0 | 0 | null | [
"safetensors",
"t5",
"region:us"
] | null | "2024-09-19T00:54:49Z" | Entry not found |
SALUTEASD/google-gemma-2b-1726707291 | SALUTEASD | "2024-09-19T00:54:50Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-19T00:54:50Z" | Entry not found |
tronsdds/Qwen-Qwen1.5-1.8B-1726707300 | tronsdds | "2024-09-19T00:55:14Z" | 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 | "2024-09-19T00:55:00Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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utahnlp/newsqa_t5-base_seed-3 | utahnlp | "2024-09-19T00:56:02Z" | 0 | 0 | null | [
"safetensors",
"t5",
"region:us"
] | null | "2024-09-19T00:55:28Z" | Entry not found |
dogssss/Qwen-Qwen1.5-1.8B-1726707357 | dogssss | "2024-09-19T00:56:01Z" | 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 | "2024-09-19T00:55:58Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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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
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[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]
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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 |
utahnlp/newsqa_t5-large_seed-1 | utahnlp | "2024-09-19T00:58:03Z" | 0 | 0 | null | [
"safetensors",
"t5",
"region:us"
] | null | "2024-09-19T00:56:13Z" | Entry not found |
GabrielTestOffcial/Morajo | GabrielTestOffcial | "2024-09-19T00:56:57Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-09-19T00:56:19Z" | ---
license: openrail
---
|
tronsdds/google-gemma-2b-1726707408 | tronsdds | "2024-09-19T00:57:24Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-19T00:56:49Z" | ---
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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### Downstream Use [optional]
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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
<!-- 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]
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#### Testing Data
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#### Metrics
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### 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:**
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**APA:**
[More Information Needed]
## Glossary [optional]
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## More Information [optional]
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## Model Card Contact
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### Framework versions
- PEFT 0.12.0 |
Vishwas1/hummingbird-base | Vishwas1 | "2024-09-19T00:59:59Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-09-19T00:57:57Z" | ---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: hummingbird-base
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. -->
# hummingbird-base
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0085
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0251 | 1.0 | 63 | 0.0203 |
| 0.0116 | 2.0 | 126 | 0.0102 |
| 0.009 | 3.0 | 189 | 0.0085 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
|
utahnlp/newsqa_t5-large_seed-2 | utahnlp | "2024-09-19T00:59:58Z" | 0 | 0 | null | [
"safetensors",
"t5",
"region:us"
] | null | "2024-09-19T00:58:13Z" | Entry not found |
huazi123/Qwen-Qwen1.5-1.8B-1726707557 | huazi123 | "2024-09-19T00:59:19Z" | 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 | "2024-09-19T00:59:15Z" | ---
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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## 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]
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[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
- **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
<!-- 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 |
utahnlp/newsqa_t5-large_seed-3 | utahnlp | "2024-09-19T01:01:52Z" | 0 | 0 | null | [
"safetensors",
"t5",
"region:us"
] | null | "2024-09-19T01:00:09Z" | Entry not found |
Krabat/Qwen-Qwen1.5-1.8B-1726707629 | Krabat | "2024-09-19T01:00:31Z" | 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 | "2024-09-19T01:00:29Z" | ---
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]
- **Funded by [optional]:** [More Information Needed]
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### 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
- **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
<!-- 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]
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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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[More Information Needed]
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[More Information Needed]
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### Framework versions
- PEFT 0.12.0 |
dogssss/Qwen-Qwen1.5-0.5B-1726707631 | dogssss | "2024-09-19T01:00:34Z" | 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 | "2024-09-19T01:00:31Z" | ---
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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- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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[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. -->
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<!-- 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]
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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-1726707632 | tronsdds | "2024-09-19T01:00:32Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-19T01:00:32Z" | Entry not found |
SALUTEASD/Qwen-Qwen1.5-0.5B-1726707693 | SALUTEASD | "2024-09-19T01:01:38Z" | 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 | "2024-09-19T01:01:32Z" | ---
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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<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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[More Information Needed]
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peakji/qwen2.5-0.5b-instruct-trim | peakji | "2024-09-19T01:02:13Z" | 0 | 0 | null | [
"safetensors",
"qwen2",
"region:us"
] | null | "2024-09-19T01:01:40Z" | Entry not found |
rana-shahroz/mistral-7b-openorca-lora-r8-gsm8k-epochs1-adapter | rana-shahroz | "2024-09-19T01:02:09Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-09-19T01:02:05Z" | ---
library_name: transformers
tags: []
---
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yunhuijang/p0n4qgai | yunhuijang | "2024-09-19T01:02:13Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-19T01:02:13Z" | Entry not found |
utahnlp/newsqa_t5-3b_seed-1 | utahnlp | "2024-09-19T01:05:40Z" | 0 | 0 | null | [
"safetensors",
"t5",
"region:us"
] | null | "2024-09-19T01:02:26Z" | Entry not found |
tronsdds/Qwen-Qwen1.5-1.8B-1726707773 | tronsdds | "2024-09-19T01:03:05Z" | 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 | "2024-09-19T01:02:53Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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Krabat/google-gemma-2b-1726707796 | Krabat | "2024-09-19T01:03:20Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-19T01:03:16Z" | ---
base_model: google/gemma-2b
library_name: peft
---
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tronsdds/google-gemma-2b-1726707881 | tronsdds | "2024-09-19T01:05:16Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-19T01:04:41Z" | ---
base_model: google/gemma-2b
library_name: peft
---
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yunhuijang/qnu3tc47 | yunhuijang | "2024-09-19T03:49:54Z" | 0 | 0 | null | [
"safetensors",
"t5",
"region:us"
] | null | "2024-09-19T01:04:47Z" | Entry not found |
SALUTEASD/Qwen-Qwen1.5-1.8B-1726707908 | SALUTEASD | "2024-09-19T01:05:07Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-19T01:05:07Z" | Entry not found |
SHLIM05/VIT | SHLIM05 | "2024-09-19T01:05:15Z" | 0 | 0 | null | [
"pytorch",
"vit",
"region:us"
] | null | "2024-09-19T01:05:08Z" | Entry not found |
dogssss/Qwen-Qwen1.5-1.8B-1726707935 | dogssss | "2024-09-19T01:05:38Z" | 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 | "2024-09-19T01:05:36Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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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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## Glossary [optional]
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### Framework versions
- PEFT 0.12.0 |