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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[project]
name = "maincoder-1b-local"
version = "0.1.0"
requires-python = ">=3.14"
dependencies = [
    "accelerate>=1.14.0",
    "safetensors>=0.7.0",
    "tokenizers>=0.22.2",
    "torch>=2.13.0",
    "transformers>=4.57.3",
]

[tool.uv]
# Avoid resolving artifacts uploaded in the last month.
exclude-newer = "30 days"

[tool.uv.audit]
malware-check = true

[tool.ruff]
line-length = 100
target-version = "py314"
cache-dir = ".cache/ruff"

[tool.ruff.lint]
select = ["E", "F", "I", "B", "UP"]
ignore = ["E501"]

[dependency-groups]
dev = [
    "pyrefly>=1.1.1",
    "ruff>=0.15.21",
]