med_mistral

Model Card for med_mistral

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Model Details

Model Description

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

4-bit version: med_mistral_4bit

• 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.

# !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

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Training data included 20k examples randomly selected from datasets:

• pubmed
• bigbio/czi_drsm
• bigbio/bc5cdr
• bigbio/distemist
• pubmed_qa
• medmcqa