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

clov-medchat / checkpoint-102 /adapter_model.safetensors

Alan Joshua

Upload fine-tuned Mistral model

500065d verified 11 months ago

Download

History Blame Contribute Delete

336 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 0f9e570162b1ea1f05f87a644a4e6341a879fa7259500ca0fe76c31bf6b21383
Size of remote file:: 336 MB
SHA256:: 01bd6c57099649d6ea07acc063f25cfb203a2b7f435ac6ee302c867cbadf6edb

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.