---
language:
- en
- fr
- nl
- es
- it
- pl
- ro
- de
license: apache-2.0
library_name: transformers
tags:
- mergekit
- merge
- dare
- medical
- biology
- mlx
datasets:
- pubmed
base_model:
- BioMistral/BioMistral-7B
- mistralai/Mistral-7B-Instruct-v0.1
pipeline_tag: text-generation
---

# abhishek-ch/biomistral-7b-synthetic-ehr
This model was converted to MLX format from [`BioMistral/BioMistral-7B-DARE`]().
Refer to the [original model card](https://huggingface.co/BioMistral/BioMistral-7B-DARE) for more details on the model.


## Use with mlx

```bash
pip install mlx-lm
```

The model was fine-tuned on [health_facts](https://huggingface.co/datasets/health_fact) and Synthetic EHR dataset inspired by MIMIC-IV, for 1000 steps using mlx

```python
def format_prompt(prompt:str, question: str) -> str:
    return """<s>[INST]
## Instructions
{}
## User Question
{}.
[/INST]</s> 
""".format(prompt, question)
```

Example For EHR Diagnosis
```
Prompt = """You are an expert in provide diagnosis summary based on clinical notes.
Objective: Your task is to generate concise summaries of the diagnosis, focusing on critical information"""
```

Example for Healthfacts Check
```
Prompt: You are a Public Health AI Assistant. You can do the fact-checking of public health claims. \nEach answer labelled with true, false, unproven or mixture. \nPlease provide the reason behind the answer
```

## Model Loading Using mlx

```python
from mlx_lm import generate, load
model, tokenizer = load("abhishek-ch/biomistral-7b-synthetic-ehr")
response = generate(
    fused_model,
    fused_tokenizer,
    prompt=format_prompt(prompt, question),
    verbose=True,  # Set to True to see the prompt and response
    temp=0.0,
    max_tokens=512,
)
```