Instructions to use varma007ut/Medical_Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use varma007ut/Medical_Chat with Transformers:

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

pipe = pipeline("text-generation", model="varma007ut/Medical_Chat")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("varma007ut/Medical_Chat")
model = AutoModelForCausalLM.from_pretrained("varma007ut/Medical_Chat", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use varma007ut/Medical_Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "varma007ut/Medical_Chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "varma007ut/Medical_Chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/varma007ut/Medical_Chat

SGLang

How to use varma007ut/Medical_Chat 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 "varma007ut/Medical_Chat" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "varma007ut/Medical_Chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "varma007ut/Medical_Chat" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "varma007ut/Medical_Chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use varma007ut/Medical_Chat 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 varma007ut/Medical_Chat 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 varma007ut/Medical_Chat to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for varma007ut/Medical_Chat to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="varma007ut/Medical_Chat",
    max_seq_length=2048,
)

Docker Model Runner
How to use varma007ut/Medical_Chat with Docker Model Runner:
```
docker model run hf.co/varma007ut/Medical_Chat
```

Model README

Model Overview

Model Name: Medicine_chat
Base Model: unsloth/meta-llama-3.1-8b-bnb-4bit
Developed by: varma007ut
License: Apache 2.0

Description

This model is a fine-tuned version of the unsloth/meta-llama-3.1-8b-bnb-4bit designed specifically for text generation tasks in the medical domain. It leverages a substantial dataset of medical texts to improve its performance and relevance in generating medical-related content.

Fine-tuning Details

Fine-tuned Data: The model has been fine-tuned on medicinal data, enhancing its ability to understand and generate contextually appropriate medical text.
Objective: The fine-tuning process aims to make the model proficient in medical terminology, guidelines, and general knowledge pertinent to healthcare professionals.

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