transformers safetensors mistral text-generation trl sft conversational dataset:pubmed dataset:bigbio/czi_drsm dataset:bigbio/bc5cdr dataset:bigbio/distemist dataset:pubmed_qa dataset:medmcqa license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference 4-bit bitsandbytes region:us
---
license: apache-2.0
library_name: transformers
tags:
- trl
- sft
datasets:
- pubmed
- bigbio/czi_drsm
- bigbio/bc5cdr
- bigbio/distemist
- pubmed_qa
- medmcqa
---

# Model Card for med_mistral_4bit

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

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Model 4-bit Mistral-7B-Instruct-v0.2 finetuned with QLoRA on multiple medical datasets.

16-bit version: [med_mistral](https://huggingface.co/adriata/med_mistral)

- **License:** apache-2.0
- **Finetuned from model :** mistralai/Mistral-7B-Instruct-v0.2

### Model Sources [optional]

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- **Repository:** https://github.com/atadria/med_llm

## Uses

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The model is finetuned on medical data and is intended only for research. It should not be used as a substitute for professional medical advice, diagnosis, or treatment.

## Bias, Risks, and Limitations

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The model's predictions are based on the information available in the finetuned medical dataset. It may not generalize well to all medical conditions or diverse patient populations.

Sensitivity to variations in input data and potential biases present in the training data may impact the model's performance.

### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

```python
# !pip install -q transformers accelerate bitsandbytes

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("adriata/med_mistral")
model = AutoModelForCausalLM.from_pretrained("adriata/med_mistral")

prompt_template = """<s>[INST] {prompt} [/INST]"""

prompt = "What is influenza?"

model_inputs = tokenizer.encode(prompt_template.format(prompt=prompt),
                                return_tensors="pt").to("cuda")

generated_ids = model.generate(model_inputs, max_new_tokens=512, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```

## Training Details
~13h - 20k examples x 1 epoch 

GPU: OVH - 1 × NVIDIA TESLA V100S (32 GiB RAM) 

### 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. -->
Training data included 20k examples randomly selected from datasets:
- pubmed
- bigbio/czi_drsm
- bigbio/bc5cdr
- bigbio/distemist
- pubmed_qa
- medmcqa
