Instructions to use shri171981/medical_chat_generative with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shri171981/medical_chat_generative with Transformers:

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

pipe = pipeline("text-generation", model="shri171981/medical_chat_generative")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shri171981/medical_chat_generative")
model = AutoModelForCausalLM.from_pretrained("shri171981/medical_chat_generative", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use shri171981/medical_chat_generative with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shri171981/medical_chat_generative"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shri171981/medical_chat_generative",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shri171981/medical_chat_generative

SGLang

How to use shri171981/medical_chat_generative 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 "shri171981/medical_chat_generative" \
    --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": "shri171981/medical_chat_generative",
		"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 "shri171981/medical_chat_generative" \
        --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": "shri171981/medical_chat_generative",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use shri171981/medical_chat_generative with Docker Model Runner:
```
docker model run hf.co/shri171981/medical_chat_generative
```

HACK_DOC (Medical Assistant)

This is a Llama-3 based medical assistant model. It was fine-tuned on the ChatDoctor-HealthCareMagic-100k dataset to provide empathetic, doctor-style responses to medical queries.

Model Details

Base Model: unsloth/llama-3-8b-instruct-bnb-4bit
Adapter Type: LoRA (Rank 64)
Training Framework: Unsloth / PyTorch
Trigger Phrase: The model is trained to start responses with: "THANKS FOR ASKING HACK_DOC. Here is my answer:"

How to use

You must load this adapter on top of the base Llama-3 model.

from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

# 1. Load Base Model
base_model_name = "unsloth/llama-3-8b-instruct-bnb-4bit"
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    load_in_4bit=True,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(base_model_name)

# 2. Load HACK_DOC Adapter
model = PeftModel.from_pretrained(base_model, "shri171981/genai_hack_doc")

# 3. Run Inference
inputs = tokenizer("I have a severe headache.", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Safetensors

Model size

8B params

Tensor type

F16

F32

shri171981
/

medical_chat_generative

HACK_DOC (Medical Assistant)

Model Details

How to use

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