---
library_name: peft
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
datasets:
- ruslanmv/ai-medical-chatbot
license: apache-2.0
---

# Model Card for Medical-Mixtral-7B-v1.5k

[![](future.jpg)](https://ruslanmv.com/)



### Model Description

The Medical-Mixtral-7B-v1.5k is a fine-tuned Mixtral model for answering medical assistance questions. This model is a novel version of mistralai/Mixtral-8x7B-Instruct-v0.1, adapted to a subset of 1.5k records from the AI Medical Chatbot dataset, which contains 250k records. The purpose of this model is to provide a ready chatbot to answer questions related to medical assistance.

### Model Sources [optional]



## How to Get Started with the Model

Installation

```
pip install -qU  transformers==4.36.2  datasets python-dotenv peft bitsandbytes accelerate 
```

Use the code below to get started with the model.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, logging, BitsAndBytesConfig
import os, torch

# Define the name of your fine-tuned model
finetuned_model = 'ruslanmv/Medical-Mixtral-7B-v1.5k'

# Load fine-tuned model
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=False,
)
model_pretrained = AutoModelForCausalLM.from_pretrained(
    finetuned_model,
    load_in_4bit=True,
    quantization_config=bnb_config,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(finetuned_model, trust_remote_code=True)

# Set pad_token_id to eos_token_id
model_pretrained.config.pad_token_id = tokenizer.eos_token_id

pipe = pipeline(task="text-generation", model=model_pretrained, tokenizer=tokenizer, max_length=100)

def build_prompt(question):
  prompt=f"[INST]@Enlighten. {question} [/INST]"
  return prompt

question = "What does abutment of the nerve root mean?"
prompt = build_prompt(question)

# Generate text based on the prompt
result = pipe(prompt)[0]
generated_text = result['generated_text']

# Remove the prompt from the generated text
generated_text = generated_text.replace(prompt, "", 1).strip()

print(generated_text)
```



### Framework versions

- PEFT 0.10.0

### Furter information
[https://ruslanmv.com/)](https://ruslanmv.com/)