Instructions to use prithivMLmods/Llama-Doctor-3.2-3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Llama-Doctor-3.2-3B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Llama-Doctor-3.2-3B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Llama-Doctor-3.2-3B-Instruct")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Llama-Doctor-3.2-3B-Instruct")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use prithivMLmods/Llama-Doctor-3.2-3B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Llama-Doctor-3.2-3B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Llama-Doctor-3.2-3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Llama-Doctor-3.2-3B-Instruct

SGLang

How to use prithivMLmods/Llama-Doctor-3.2-3B-Instruct 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 "prithivMLmods/Llama-Doctor-3.2-3B-Instruct" \
    --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": "prithivMLmods/Llama-Doctor-3.2-3B-Instruct",
		"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 "prithivMLmods/Llama-Doctor-3.2-3B-Instruct" \
        --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": "prithivMLmods/Llama-Doctor-3.2-3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Llama-Doctor-3.2-3B-Instruct with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Llama-Doctor-3.2-3B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Llama-Doctor-3.2-3B-Instruct Modelfile

The Llama-Doctor-3.2-3B-Instruct model is designed for text generation tasks, particularly in contexts where instruction-following capabilities are needed. This model is a fine-tuned version of the base Llama-3.2-3B-Instruct model and is optimized for understanding and responding to user-provided instructions or prompts. The model has been trained on a specialized dataset, avaliev/chat_doctor, to enhance its performance in providing conversational or advisory responses, especially in medical or technical fields.

File Name { Chat Doctor }	Size	Description	Upload Status
`.gitattributes`	1.57 kB	Git attributes file	Uploaded
`README.md`	263 Bytes	README file	Uploaded
`config.json`	1.03 kB	Model configuration	Uploaded
`generation_config.json`	248 Bytes	Generation configuration	Uploaded
`pytorch_model-00001-of-00002.bin`	4.97 GB	PyTorch model file (part 1 of 2)	Uploaded (LFS)
`pytorch_model-00002-of-00002.bin`	1.46 GB	PyTorch model file (part 2 of 2)	Uploaded (LFS)
`pytorch_model.bin.index.json`	21.2 kB	Index for PyTorch model	Uploaded
`special_tokens_map.json`	477 Bytes	Special tokens map	Uploaded
`tokenizer.json`	17.2 MB	Tokenizer file	Uploaded (LFS)
`tokenizer_config.json`	57.4 kB	Tokenizer configuration	Uploaded

Model Type	Size	Context Length	Link
GGUF	3B	-	🤗 Llama-Doctor-3.2-3B-Instruct-GGUF

Key Use Cases:

Conversational AI: Engage in dialogue, answering questions, or providing responses based on user instructions.
Text Generation: Generate content, summaries, explanations, or solutions to problems based on given prompts.
Instruction Following: Understand and execute instructions, potentially in complex or specialized domains like medical, technical, or academic fields.

The model leverages a PyTorch-based architecture and comes with various files such as configuration files, tokenizer files, and special tokens maps to facilitate smooth deployment and interaction.

Intended Applications:

Chatbots for customer support or virtual assistants.
Medical Consultation Tools for generating advice or answering medical queries (given its training on the chat_doctor dataset).
Content Creation tools, helping generate text based on specific instructions.
Problem-solving Assistants that offer explanations or answers to user queries, particularly in instructional contexts.