---
license: cc-by-nc-3.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- axolotl
- generated_from_trainer
model-index:
- name: postOpTranscriptAnalyzer
  results: []
---

# postOpTranscriptAnalyzer

This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) .
It achieves the following results on the evaluation set:
- Loss: 0.8360

## Model description

The purpose of this model is to analyze the performance of healthcare professionals in providing post-operative instructions, based on a transcript of their conversations.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9739        | 0.1739 | 1    | 0.9650          |
| 0.8749        | 0.3478 | 2    | 0.9442          |
| 0.896         | 0.6957 | 4    | 0.8696          |
| 0.8791        | 1.0435 | 6    | 0.8514          |
| 0.8063        | 1.3043 | 8    | 0.8326          |
| 0.7643        | 1.6522 | 10   | 0.8248          |
| 0.8239        | 2.0    | 12   | 0.8203          |
| 0.6914        | 2.2609 | 14   | 0.8261          |
| 0.6751        | 2.6087 | 16   | 0.8356          |
| 0.6157        | 2.9565 | 18   | 0.8357          |
| 0.5871        | 3.2174 | 20   | 0.8360          |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.1.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1

<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
base_model: mistralai/Mistral-7B-v0.1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: mhenrichsen/alpaca_2k_test
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./out

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000005

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

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: '<s>'
  eos_token: '</s>'
  unk_token: '<unk>'

hub_model_id: bmezgebu/postOpTranscriptAnalyzer
push_to_hub: True

```

</details><br>