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")

Notebooks
Google Colab
Kaggle
Local Apps

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 new

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
```

varma007ut commited on Sep 23, 2024

Commit

334d76e

verified ·

1 Parent(s): 5b7116a

Update README.md

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@@ -33,26 +33,4 @@ This model is a fine-tuned version of the `unsloth/meta-llama-3.1-8b-bnb-4bit` d
 To use this model, ensure you have the necessary libraries installed. You can install them using pip:
-```bash
-pip install transformers
-## Usage
-Here’s an example of how to load and use the model for text generation:
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-model_name = "your_model_name"  # Replace with your model's name
-# Load model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
-# Generate text
-input_text = "What are the symptoms of diabetes?"
-input_ids = tokenizer.encode(input_text, return_tensors='pt')
-output = model.generate(input_ids, max_length=150)
-generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
-print(generated_text)


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34	To use this model, ensure you have the necessary libraries installed. You can install them using pip:
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