Instructions to use yasserrmd/DentaInstruct-1.2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yasserrmd/DentaInstruct-1.2B with Transformers:

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

pipe = pipeline("text-generation", model="yasserrmd/DentaInstruct-1.2B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yasserrmd/DentaInstruct-1.2B")
model = AutoModelForCausalLM.from_pretrained("yasserrmd/DentaInstruct-1.2B", device_map="auto")
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 yasserrmd/DentaInstruct-1.2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yasserrmd/DentaInstruct-1.2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yasserrmd/DentaInstruct-1.2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yasserrmd/DentaInstruct-1.2B

SGLang

How to use yasserrmd/DentaInstruct-1.2B 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 "yasserrmd/DentaInstruct-1.2B" \
    --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": "yasserrmd/DentaInstruct-1.2B",
		"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 "yasserrmd/DentaInstruct-1.2B" \
        --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": "yasserrmd/DentaInstruct-1.2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use yasserrmd/DentaInstruct-1.2B 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 yasserrmd/DentaInstruct-1.2B 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 yasserrmd/DentaInstruct-1.2B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for yasserrmd/DentaInstruct-1.2B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="yasserrmd/DentaInstruct-1.2B",
    max_seq_length=2048,
)

Docker Model Runner
How to use yasserrmd/DentaInstruct-1.2B with Docker Model Runner:
```
docker model run hf.co/yasserrmd/DentaInstruct-1.2B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🦷 DentaInstruct-1.2B

DentaInstruct-1.2B is a fine-tuned instruction-following language model designed to assist with dental domain queries. It was trained using clinically focused questions and answers from the miriad/miriad-4.4M dataset, specifically filtered for the Dental & Oral Medicine specialty. The base model used is LiquidAI/LFM2-1.2B, optimized with the Unsloth fine-tuning library.

Model Details

Architecture: LFM2-1.2B (decoder-only transformer)
Base Model: LiquidAI/LFM2-1.2B
Fine-tuning Library: Unsloth
Instruction Format: Chat-style, formatted using apply_chat_template
Trained On: Dental subset of miriad-4.4M
Compute: Trained using Google Colab T4 (free tier)

Benchmark Summary

The model was evaluated on a curated set of dental prompts with rich terminology across endodontics, periodontics, prosthodontics, and oral surgery. Responses were assessed for fluency, accuracy, and domain relevance.

✅ Terminology Handling: Excellent coverage of dental-specific terms
✅ Instruction Following: Clear, context-aware responses
✅ Answer Structure: Consistently professional and clinically coherent
⚠️ Minor Hallucinations: A few outputs demonstrated factual drift in rare cases

Limitations & Warnings

This model was fine-tuned using the MIRIAD dataset, which comes with the following caution:

This model is trained on data that has not been manually reviewed by medical experts. It should not be used for diagnostic purposes or to inform medical decision-making. It is intended for research and educational purposes only.

Not a substitute for professional dental care
Do not use this model for clinical diagnosis or treatment advice

Intended Use

Educational Q&A for dental students
Conversational chatbots focused on oral health
Research on clinical-domain instruction tuning

Citation

If you use this model or parts of it, please consider citing:

@misc{miriad2024,
  title={MIRIAD: Medical Instructional Record with Interactions and Answers Dataset},
  author={Xue, Yutong and others},
  year={2024},
  url={https://huggingface.co/datasets/miriad/miriad-4.4M}
}

Acknowledgements

LiquidAI for the LFM2 model series
Unsloth for training acceleration
MIRIAD authors for the publicly available medical dataset

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Safetensors

Model size

1B params

Tensor type

BF16

F16

Model tree for yasserrmd/DentaInstruct-1.2B

Base model

LiquidAI/LFM2-1.2B

Finetuned

(67)

this model

Quantizations

3 models

yasserrmd
/

DentaInstruct-1.2B