Instructions to use kshitij230/LLAMA-DISEASE-CURE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kshitij230/LLAMA-DISEASE-CURE with Transformers:

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

pipe = pipeline("text-generation", model="kshitij230/LLAMA-DISEASE-CURE")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kshitij230/LLAMA-DISEASE-CURE")
model = AutoModelForCausalLM.from_pretrained("kshitij230/LLAMA-DISEASE-CURE")
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

vLLM

How to use kshitij230/LLAMA-DISEASE-CURE with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kshitij230/LLAMA-DISEASE-CURE"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kshitij230/LLAMA-DISEASE-CURE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kshitij230/LLAMA-DISEASE-CURE

SGLang

How to use kshitij230/LLAMA-DISEASE-CURE 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 "kshitij230/LLAMA-DISEASE-CURE" \
    --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": "kshitij230/LLAMA-DISEASE-CURE",
		"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 "kshitij230/LLAMA-DISEASE-CURE" \
        --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": "kshitij230/LLAMA-DISEASE-CURE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use kshitij230/LLAMA-DISEASE-CURE 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 kshitij230/LLAMA-DISEASE-CURE 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 kshitij230/LLAMA-DISEASE-CURE to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kshitij230/LLAMA-DISEASE-CURE to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="kshitij230/LLAMA-DISEASE-CURE",
    max_seq_length=2048,
)

Docker Model Runner
How to use kshitij230/LLAMA-DISEASE-CURE with Docker Model Runner:
```
docker model run hf.co/kshitij230/LLAMA-DISEASE-CURE
```

Model Card for LLAMA-DISEASE-CURE

LLAMA-DISEASE-CURE is a fine-tuned version of the LLaMA-3 8B model optimized for disease classification and suggesting potential cures based on patient textual input. This model helps automate the mapping of symptoms to diseases and treatment strategies, enabling applications in AI-powered clinical decision support tools.

Model Details

Model Description

This is the model card of a 🤗 Transformers model pushed to the Hub by Kshitij Sharma. It has been fine-tuned using Unsloth’s efficient low-bit training (4-bit quantization) on a medical dataset containing patient symptoms and corresponding diseases with treatments.

Developed by: Kshitij Sharma
Funded by [optional]: Self-funded
Shared by [optional]: Kshitij Sharma
Model type: Text Classification (Medical NLP)
Language(s) (NLP): English
License: Apache 2.0
Finetuned from model [optional]: unsloth/llama-3-8b-bnb-4bit

Model Sources [optional]

Repository: https://huggingface.co/kshitij230/LLAMA-DISEASE-CURE
Paper [optional]: N/A
Demo [optional]: Coming soon

Uses

Direct Use

Text classification of patient-reported symptoms into disease categories
Generation of suggested cures or treatments based on classified disease

Downstream Use [optional]

Integration into clinical assistants or triage bots
Medical report preprocessing or symptom understanding tools
Telemedicine AI assistant solutions

Out-of-Scope Use

Should not be used for critical, real-time medical diagnosis
Not a substitute for licensed medical professionals
Should not be used in emergencies or for prescribing medication

Bias, Risks, and Limitations

Limited by the coverage and quality of the dataset used
May not generalize well to rare diseases or symptoms expressed in colloquial terms
May contain biases present in training data (e.g., demographic or linguistic)

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. It is recommended that all outputs are reviewed by qualified healthcare professionals before clinical use.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import pipeline

classifier = pipeline("text-classification", model="kshitij230/LLAMA-DISEASE-CURE")
output = classifier("Patient reports shortness of breath, chest pain, and dizziness.")
print(output)

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