Instructions to use CloveAI/clov-medchat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CloveAI/clov-medchat with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/mistral-7b-instruct-v0.2-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "CloveAI/clov-medchat")

Transformers

How to use CloveAI/clov-medchat with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="CloveAI/clov-medchat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("CloveAI/clov-medchat", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use CloveAI/clov-medchat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CloveAI/clov-medchat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CloveAI/clov-medchat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CloveAI/clov-medchat

SGLang

How to use CloveAI/clov-medchat with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "CloveAI/clov-medchat" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CloveAI/clov-medchat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "CloveAI/clov-medchat" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CloveAI/clov-medchat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use CloveAI/clov-medchat with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for CloveAI/clov-medchat to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for CloveAI/clov-medchat to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for CloveAI/clov-medchat to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="CloveAI/clov-medchat",
    max_seq_length=2048,
)

Docker Model Runner
How to use CloveAI/clov-medchat with Docker Model Runner:
```
docker model run hf.co/CloveAI/clov-medchat
```

Use this model

!pip install -q --upgrade bitsandbytes transformers accelerate

from transformers import pipeline

pipe = pipeline("text-generation", model="alanjoshua2005/alan-mistral-finetuned")

user_input = input("Enter your medical question or prompt: ")

prompt = (
    f"""Imagine you are a helpful medical chatbot. Respond based on the user input below:

<s>[INST] {user_input} [/INST]

Please provide your answer in **structured Markdown format**. Follow these rules:
- Complete the answer fully; do not stop mid-sentence
- Use emojis to highlight key points
- Use horizontal lines (---) to separate sections
- Use bullet points and numbered lists where appropriate
- Use tables if necessary to organize information clearly
- Explain medical terms in simple words
- Do NOT include any links, URLs, or image references
- Make the response easy-to-read and informative
"""
)

result = pipe(
    prompt,
    max_new_tokens=512,       
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
    repetition_penalty=1.1    
)

generated_text = result[0]["generated_text"]
response = generated_text.replace(prompt, "").strip()

print(response)

Model Details

Developed by: Alan Joshua
Model type: Text-Generation
Language(s): English
License: MIT
Finetuned from model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit
Dataset: ruslanmv/ai-medical-chatbot

Model Description

This model is a medical chatbot fine-tuned on the ruslanmv/ai-medical-chatbot dataset using LoRA adapters on the Mistral 7B instruct model (4-bit). It is designed to provide accurate, easy-to-understand medical information in English.

Key features of this model include:

Structured Markdown responses: Answers are formatted using bullets, numbered lists, tables, and horizontal lines for readability.
Clear explanations: Medical terms are explained in simple words for users of all backgrounds.
Emojis: Used to highlight key points and make responses more engaging.
No links or images: Ensures responses remain text-only for safe, direct answers.
Complete answers: Designed to generate full, coherent responses without cutting off mid-sentence.

This model is suitable for educational purposes, healthcare awareness, and interactive Q&A applications. It is not a substitute for professional medical advice. Always verify information with a qualified healthcare provider.

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unsloth/mistral-7b-instruct-v0.2-bnb-4bit

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