Instructions to use BirdieByte1024/Qwen2.5-1.5B-dental-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use BirdieByte1024/Qwen2.5-1.5B-dental-full with PEFT:
```
Task type is invalid.
```

How to use BirdieByte1024/Qwen2.5-1.5B-dental-full with Transformers:

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

pipe = pipeline("text-generation", model="BirdieByte1024/Qwen2.5-1.5B-dental-full")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BirdieByte1024/Qwen2.5-1.5B-dental-full")
model = AutoModelForCausalLM.from_pretrained("BirdieByte1024/Qwen2.5-1.5B-dental-full")
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 BirdieByte1024/Qwen2.5-1.5B-dental-full with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BirdieByte1024/Qwen2.5-1.5B-dental-full"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BirdieByte1024/Qwen2.5-1.5B-dental-full",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BirdieByte1024/Qwen2.5-1.5B-dental-full

SGLang

How to use BirdieByte1024/Qwen2.5-1.5B-dental-full 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 "BirdieByte1024/Qwen2.5-1.5B-dental-full" \
    --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": "BirdieByte1024/Qwen2.5-1.5B-dental-full",
		"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 "BirdieByte1024/Qwen2.5-1.5B-dental-full" \
        --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": "BirdieByte1024/Qwen2.5-1.5B-dental-full",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BirdieByte1024/Qwen2.5-1.5B-dental-full with Docker Model Runner:
```
docker model run hf.co/BirdieByte1024/Qwen2.5-1.5B-dental-full
```

🦷 Qwen2.5-1.5B Dental Model (LoRA Adapter & Full Model)

This model is a fine-tuned version of Qwen/Qwen2.5-1.5B, trained with LoRA adapters on a dental procedure instruction dataset. It has been trained to explain ADA dental procedure codes in a way patients can understand.

Two versions are available:

BirdieByte1024/Qwen2.5-1.5B-LoRA-dental: LoRA adapter only
BirdieByte1024/Qwen2.5-1.5B-dental-full: Fully merged standalone model

🔍 Model Details

Base model: Qwen/Qwen2.5-1.5B
Training method: PEFT (LoRA)
Tokenizer: Inherited from base model
Model size: 1.5B parameters
Precision: fp32
Training hardware: GTX 1060 (6GB)

📚 Dataset

This model was trained on:

TachyHealth/ADA_Dental_Code_to_SBS_V2

The dataset includes ADA codes and short/long descriptions useful for patient-friendly explanations.

💬 Prompt Format

This is an instruction-tuned model using a simple text format:

### Instruction:
Explain the following dental code.

### Code:
D7140 - Extraction, erupted tooth

### Response:

✅ How to Use (Transformers)

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "BirdieByte1024/Qwen2.5-1.5B-dental-full"  # or LoRA version with PEFT if needed

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).to("cuda")

prompt = """### Instruction:
Explain the following dental code.

### Code:
D7140 - Extraction, erupted tooth

### Response:"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🛠 Deployment Options

🖥 Local with Transformers (Python)

Use the example above with transformers for local inference.

🧠 Use with Ollama / llama.cpp (GGUF format)

To deploy via ollama, convert the merged model to GGUF and run:

ollama run qwen2.5-dental

Make sure to convert your model to GGUF first using transformers + transformers-gguf or use llama.cpp export tools.

⚠️ Limitations

English-only dental domain coverage
Not a diagnostic or real clinical system
May hallucinate or oversimplify medical terms

✍️ Author

Created by BirdieByte1024 as part of a patient-education AI project using LoRA + Qwen models.

📜 License

Apache 2.0

Downloads last month: -

Safetensors

Model size

2B params

Tensor type

F32

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