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

Libraries

How to use Aasher/TibbScholar with Transformers:

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

pipe = pipeline("text-generation", model="Aasher/TibbScholar")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Aasher/TibbScholar")
model = AutoModelForCausalLM.from_pretrained("Aasher/TibbScholar")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Aasher/TibbScholar with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Aasher/TibbScholar

SGLang

How to use Aasher/TibbScholar 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 "Aasher/TibbScholar" \
    --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": "Aasher/TibbScholar",
		"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 "Aasher/TibbScholar" \
        --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": "Aasher/TibbScholar",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio new

How to use Aasher/TibbScholar 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 Aasher/TibbScholar 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 Aasher/TibbScholar to start chatting

Using HuggingFace Spaces for Unsloth

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

Load model with FastModel

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

Docker Model Runner
How to use Aasher/TibbScholar with Docker Model Runner:
```
docker model run hf.co/Aasher/TibbScholar
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🧠 TibbScholar

TibbScholar is a specialized, 3-billion-parameter language model fine-tuned for medical question-answering. It is based on Meta's Llama-3.2-3B model and is designed to serve as an informational tool for educational and research purposes.

This model was trained on a large subset of the MIRIAD-4.4M dataset to provide concise, structured answers to medical queries.

📌 Model Details

Base Model: unsloth/Llama-3.2-3B-unsloth-bnb-4bit
Fine-tuning Dataset: A 100,000 record subset of MIRIAD-4.4M.
Prompt Format: A simple Question/Answer structure (see below).
Training Framework: Fine-tuned using Unsloth AI's library with QLoRA for efficient training.

📋 Prompt Format

For the model to perform as expected, prompts must follow the structure it was trained on. The prompt should end with Answer: followed by a newline.

Question:
What are the risks in dental implant surgery?

Answer:

💡 How to Use

The model can be easily loaded using the transformers library.

from transformers import pipeline
import torch

pipe = pipeline(
    "text-generation",
    model="Aasher/TibbScholar",
    torch_dtype=torch.bfloat16, # Or float16 for older GPUs
    device_map="auto",
)

prompt = """Question:
What are the risks in dental implant surgery?

Answer:
"""

response = pipe(
    prompt,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.5,
    top_p=0.9,
)

print(response[0]["generated_text"])

⚠️ Intended Use and Limitations

This model is intended solely for academic and informational purposes. It can be a helpful tool for students and researchers exploring medical topics. This model is NOT a medical professional.

The knowledge is limited to its training data and may not be up-to-date.
It can generate incorrect or incomplete information (hallucinate).
Do not use its outputs for clinical decision-making, diagnosis, or treatment.

🚨 Disclaimer: Not for Medical Advice

The information provided by TibbScholar is not a substitute for professional medical advice. Always consult a qualified healthcare provider with any medical questions. The creator of this model assumes no liability for any actions taken based on its output.

🤝 Acknowledgements

Base Model: Meta AI for the Llama 3.2 model.
Dataset: The creators of the MIRIAD dataset.
Training: The Unsloth AI team for their excellent fine-tuning library.

Downloads last month: 2

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for Aasher/TibbScholar

Base model

meta-llama/Llama-3.2-3B-Instruct

Quantized

unsloth/Llama-3.2-3B-unsloth-bnb-4bit

Finetuned

(2)

this model

Quantizations

2 models

Aasher
/

TibbScholar