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

Libraries

How to use vyshnaviprasad/diabetic_modelf with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("vyshnaviprasad/diabetic_modelf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vyshnaviprasad/diabetic_modelf with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/vyshnaviprasad/diabetic_modelf

SGLang

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

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

Hypertension LoRA Adapter

Developed by: vyshnaviprasad
License: apache-2.0
Finetuned from model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit

This LLaMA model was trained 2x faster with Unsloth and Hugging Face's TRL library.

⚠️ Important

This is a LoRA adapter, not a full standalone model.

On Hugging Face Inference API, the pipeline_tag and base_model allow it to work directly.
Locally, you must load it with the base model for text generation.

How to Use Locally

from unsloth import FastLanguageModel

base_model = "unsloth/meta-llama-3.1-8b-instruct-bnb-4bit"
adapter_model = "vyshnaviprasad/diabetic_modelf"

# Load base model with LoRA adapter
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=base_model,
    adapter_name=adapter_model,
    load_in_4bit=True
)

FastLanguageModel.for_inference(model)

# Generate text
prompt = "Explain hypertension in simple words to a patient."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=1000)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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