Instructions to use meditsolutions/Llama-3.2-SUN-2.5B-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meditsolutions/Llama-3.2-SUN-2.5B-chat with Transformers:

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

pipe = pipeline("text-generation", model="meditsolutions/Llama-3.2-SUN-2.5B-chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("meditsolutions/Llama-3.2-SUN-2.5B-chat")
model = AutoModelForMultimodalLM.from_pretrained("meditsolutions/Llama-3.2-SUN-2.5B-chat")
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 meditsolutions/Llama-3.2-SUN-2.5B-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meditsolutions/Llama-3.2-SUN-2.5B-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meditsolutions/Llama-3.2-SUN-2.5B-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/meditsolutions/Llama-3.2-SUN-2.5B-chat

SGLang

How to use meditsolutions/Llama-3.2-SUN-2.5B-chat 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 "meditsolutions/Llama-3.2-SUN-2.5B-chat" \
    --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": "meditsolutions/Llama-3.2-SUN-2.5B-chat",
		"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 "meditsolutions/Llama-3.2-SUN-2.5B-chat" \
        --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": "meditsolutions/Llama-3.2-SUN-2.5B-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use meditsolutions/Llama-3.2-SUN-2.5B-chat with Docker Model Runner:
```
docker model run hf.co/meditsolutions/Llama-3.2-SUN-2.5B-chat
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

MedIT SUN 2.5B

Base Model

Llama 3.2 1B

Extended Size

1B to 2.5B parameters

Extension Method

Proprietary technique developed by MedIT Solutions

Fine-tuning

Open (or open subsets allowing for commercial use) open datasets from HF
Open (or open subsets allowing for commercial use) SFT datasets from HF

Training Status

Current version: chat-1.0.0

Key Features

Built on Llama 3.2 architecture
Expanded from 1B to 2.47B parameters
Optimized for open-ended conversations
Incorporates supervised fine-tuning for improved performance

Use Case

General conversation and task-oriented interactions

Limitations As the model is still in training, performance and capabilities may vary. Users should be aware that the model is not in its final form and may exhibit inconsistencies or limitations typical of in-progress AI models.

Disclaimer and Safety Considerations The Model is designed to be used as a smart assistant but not as a knowledge source within your applications, systems, or environments. It is not intended to provide 100% accurate answers, especially in scenarios where high precision and accuracy are