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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

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
```

Alan Joshua commited on Sep 26, 2025

Commit

d6c690b

verified ·

1 Parent(s): c10b8b4

Update README.md

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@@ -52,14 +52,29 @@ response = generated_text.replace(prompt, "").strip()
 print(response)
 ```
 ## Model Details
-- **Developed by:** Alan Joshua
-- **Model type:** Text-Generation
-- **Language(s) (NLP):** English
-- **License:** MIT
-- **Finetuned from model:** unsloth/mistral-7b-bnb-4bit
-- **Dataset:** ruslanmv/ai-medical-chatbot
-### Model Description

 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.