Instructions to use met-dev/met2-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use met-dev/met2-1.5B with Transformers:

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

pipe = pipeline("text-generation", model="met-dev/met2-1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("met-dev/met2-1.5B")
model = AutoModelForCausalLM.from_pretrained("met-dev/met2-1.5B")
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 met-dev/met2-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "met-dev/met2-1.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "met-dev/met2-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/met-dev/met2-1.5B

SGLang

How to use met-dev/met2-1.5B 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 "met-dev/met2-1.5B" \
    --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": "met-dev/met2-1.5B",
		"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 "met-dev/met2-1.5B" \
        --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": "met-dev/met2-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use met-dev/met2-1.5B with Docker Model Runner:
```
docker model run hf.co/met-dev/met2-1.5B
```

met2-1.5B

Commit History

Update README.md

2d9f2fb
verified

MET.dev commited on May 23, 2025

Update README.md

8c9c7ee
verified

MET.dev commited on May 23, 2025

Upload folder using huggingface_hub

790534f
verified

MET.dev commited on May 23, 2025

initial commit

b87e23c
verified

MET.dev commited on May 23, 2025

Commit History

Update README.md 2d9f2fb verified

Update README.md 8c9c7ee verified

Upload folder using huggingface_hub 790534f verified

initial commit b87e23c verified

Update README.md

2d9f2fb
verified

Update README.md

8c9c7ee
verified

Upload folder using huggingface_hub

790534f
verified

initial commit

b87e23c
verified