---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.3
tags:
- axolotl
- generated_from_trainer
model-index:
- name: Mistral-7B-v0.3-sarcasm-scrolls-2
  results: []
datasets:
- BEE-spoke-data/sarcasm-scrolls
language:
- en
---

<!-- 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. -->

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer

strict: false

# dataset
datasets:
    - path: BEE-spoke-data/sarcasm-scrolls
      type: completion # format from earlier
      field: text # Optional[str] default: text, field to use for completion data
val_set_size: 0.025

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
train_on_inputs: false
group_by_length: false

# WANDB
wandb_project: sarcasm-scrolls
wandb_entity: pszemraj
wandb_watch: gradients
wandb_name: Mistral-7B-v0.3-sarcasm-scrolls
hub_model_id: pszemraj/Mistral-7B-v0.3-sarcasm-scrolls-2
hub_strategy: every_save

gradient_accumulation_steps: 16
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_torch_fused # paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 2e-5

load_in_8bit: false
load_in_4bit: false
bf16: auto
fp16:
tf32: true

torch_compile: true # requires >= torch 2.0, may sometimes cause problems
torch_compile_backend: inductor # Optional[str]
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
logging_steps: 5
xformers_attention:
flash_attention: true

warmup_steps: 20
# hyperparams for freq of evals, saving, etc
evals_per_epoch: 4
saves_per_epoch: 4
save_safetensors: true
save_total_limit: 1 # Checkpoints saved at a time
output_dir: ./output-axolotl/output-model-theta
resume_from_checkpoint:


deepspeed:
weight_decay: 0.04

special_tokens:

```

</details><br>

# Mistral-7B-v0.3-sarcasm-scrolls @ ctx 4k

This model is a fine-tuned version of [mistralai/Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2825

## 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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0082 | 1    | 2.3959          |
| 2.412         | 0.2544 | 31   | 2.3363          |
| 2.3866        | 0.5087 | 62   | 2.3277          |
| 2.3204        | 0.7631 | 93   | 2.3012          |
| 2.2843        | 1.0174 | 124  | 2.2682          |
| 2.1748        | 1.2718 | 155  | 2.2425          |
| 1.6885        | 1.2349 | 186  | 2.2849          |
| 1.6834        | 1.4892 | 217  | 2.2825          |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1