Instructions to use jianna4/finetuned_disease with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jianna4/finetuned_disease with Transformers:

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

pipe = pipeline("text-generation", model="jianna4/finetuned_disease")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jianna4/finetuned_disease")
model = AutoModelForCausalLM.from_pretrained("jianna4/finetuned_disease")
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 Settings

vLLM

How to use jianna4/finetuned_disease with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jianna4/finetuned_disease"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jianna4/finetuned_disease",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jianna4/finetuned_disease

SGLang

How to use jianna4/finetuned_disease 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 "jianna4/finetuned_disease" \
    --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": "jianna4/finetuned_disease",
		"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 "jianna4/finetuned_disease" \
        --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": "jianna4/finetuned_disease",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jianna4/finetuned_disease with Docker Model Runner:
```
docker model run hf.co/jianna4/finetuned_disease
```

Fine-Tuned Falcon-7B for Medical Text Generation

This is a fine-tuned version of the Falcon-7B-Instruct model, adapted for generating medical text related to common diseases. The model has been fine-tuned using LoRA (Low-Rank Adaptation) on a dataset of medical texts.

Model Details

Base Model: tiiuae/falcon-7b-instruct
Fine-Tuning Method: LoRA (Low-Rank Adaptation)
Quantization: 4-bit (using bitsandbytes)
Training Dataset: Medical text data (common diseases)
Training Framework: PyTorch with Hugging Face Transformers
Fine-Tuning Duration: 3 epochs
Learning Rate: 1e-3
Batch Size: 2 (per device)

Usage

You can use this model for generating medical text or answering questions related to common diseases.

Using the Hugging Face Inference API

Install the transformers library:
```
pip install transformers
"""
```

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