---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: mistral-7B-MedText-epochs-5-lr-000002
  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. -->

[<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: `0.4.0`
```yaml
base_model: mistralai/Mistral-7B-v0.1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: utrgvseniorproject/medtext
    type: completion
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./mistral-7B-MedText-epochs-5-lr-000002

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:

wandb_project: mistral-7B-MedText
wandb_entity: utrgvmedai
wandb_watch:
wandb_name: mistral-7B-MedText-epochs-5-lr-000002
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 5
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000002

train_on_inputs: true
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: false
early_stopping_patience:
#resume_from_checkpoint: true
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
flash_attn_cross_entropy: false
flash_attn_rms_norm: true
flash_attn_fuse_qkv: false
flash_attn_fuse_mlp: true

warmup_steps: 100
evals_per_epoch: 4
eval_table_size:
eval_sample_packing: False
saves_per_epoch: 1
debug:
deepspeed: /home/josegomez15/axolotl/deepspeed_configs/zero2.json
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:

```

</details><br>

# mistral-7B-MedText-epochs-5-lr-000002

This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6109

## 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-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 8
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5029        | 0.02  | 1    | 1.5677          |
| 1.5892        | 0.26  | 11   | 1.5674          |
| 1.2975        | 0.51  | 22   | 1.5646          |
| 1.6405        | 0.77  | 33   | 1.5585          |
| 1.4797        | 1.02  | 44   | 1.5535          |
| 1.4285        | 1.23  | 55   | 1.5510          |
| 1.565         | 1.49  | 66   | 1.5497          |
| 1.2469        | 1.74  | 77   | 1.5485          |
| 1.6729        | 2.0   | 88   | 1.5482          |
| 1.2883        | 2.23  | 99   | 1.5585          |
| 1.2285        | 2.49  | 110  | 1.5651          |
| 1.2074        | 2.74  | 121  | 1.5639          |
| 1.1427        | 3.0   | 132  | 1.5614          |
| 1.1015        | 3.21  | 143  | 1.5898          |
| 1.0554        | 3.47  | 154  | 1.5990          |
| 1.1675        | 3.72  | 165  | 1.5823          |
| 1.0228        | 3.98  | 176  | 1.5949          |
| 1.0462        | 4.19  | 187  | 1.6039          |
| 1.0623        | 4.44  | 198  | 1.6127          |
| 1.1305        | 4.7   | 209  | 1.6109          |


### Framework versions

- Transformers 4.38.0
- Pytorch 2.0.1+cu117
- Datasets 2.17.0
- Tokenizers 0.15.0