Instructions to use MengLinMaker/Maincoder-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MengLinMaker/Maincoder-1B with Transformers:

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

pipe = pipeline("text-generation", model="MengLinMaker/Maincoder-1B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("MengLinMaker/Maincoder-1B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MengLinMaker/Maincoder-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MengLinMaker/Maincoder-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MengLinMaker/Maincoder-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MengLinMaker/Maincoder-1B

SGLang

How to use MengLinMaker/Maincoder-1B 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 "MengLinMaker/Maincoder-1B" \
    --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": "MengLinMaker/Maincoder-1B",
		"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 "MengLinMaker/Maincoder-1B" \
        --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": "MengLinMaker/Maincoder-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MengLinMaker/Maincoder-1B with Docker Model Runner:
```
docker model run hf.co/MengLinMaker/Maincoder-1B
```

Maincoder-1B

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from pathlib import Path

from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_DIR = Path(__file__).resolve().parent

model = AutoModelForCausalLM.from_pretrained(
    MODEL_DIR,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
    MODEL_DIR,
    trust_remote_code=True,
)

# Code completion example
prompt = '''"""Complete the fibonacci function in Python."""
def fibonacci(n: int) -> int:
'''

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.5,
    do_sample=True,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))