Instructions to use hackint0sh/phi-3-clinical with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hackint0sh/phi-3-clinical with Transformers:

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

pipe = pipeline("text-generation", model="hackint0sh/phi-3-clinical", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hackint0sh/phi-3-clinical", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("hackint0sh/phi-3-clinical", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hackint0sh/phi-3-clinical with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hackint0sh/phi-3-clinical"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hackint0sh/phi-3-clinical",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hackint0sh/phi-3-clinical

SGLang

How to use hackint0sh/phi-3-clinical 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 "hackint0sh/phi-3-clinical" \
    --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": "hackint0sh/phi-3-clinical",
		"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 "hackint0sh/phi-3-clinical" \
        --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": "hackint0sh/phi-3-clinical",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hackint0sh/phi-3-clinical with Docker Model Runner:
```
docker model run hf.co/hackint0sh/phi-3-clinical
```

hackint0sh commited on Jan 15, 2025

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 Here’s a sample README.md file for your Hugging Face model repository:
-# 🤖 [Phi-3-Clinical]
 Welcome to the repository for Phi-3-Clinical, a fine-tuned model designed to empower medical researchers and developers in the Bio-Pharma domain. This model has been meticulously trained on clinical trial datasets from the U.S. government to deliver high-quality insights and facilitate research and development in healthcare and pharmaceutical innovation. This model is currently being actively updated and improved as part of my ongoing research and work in Retrieval-Augmented Generation (RAG).

 Here’s a sample README.md file for your Hugging Face model repository:
+# 🤖 Phi-3-Clinical
 Welcome to the repository for Phi-3-Clinical, a fine-tuned model designed to empower medical researchers and developers in the Bio-Pharma domain. This model has been meticulously trained on clinical trial datasets from the U.S. government to deliver high-quality insights and facilitate research and development in healthcare and pharmaceutical innovation. This model is currently being actively updated and improved as part of my ongoing research and work in Retrieval-Augmented Generation (RAG).