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
```

MengLinMaker commited on about 18 hours ago

Commit

1403f74

1 Parent(s): 9f8a1b6

chore: install pytorch and dependencies

Browse files

Files changed (3) hide show

pyproject.toml +2 -0
run.py +26 -0
uv.lock +390 -0

pyproject.toml CHANGED Viewed

@@ -3,8 +3,10 @@ name = "maincoder-1b-local"
 version = "0.1.0"
 requires-python = ">=3.14"
 dependencies = [
     "safetensors>=0.7.0",
     "tokenizers>=0.22.2",
     "transformers>=4.57.3",
 ]

 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",
 ]

run.py ADDED Viewed

	@@ -0,0 +1,26 @@

+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    "MengLinMaker/Maincoder-1B",
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True,
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "MengLinMaker/Maincoder-1B",
+    trust_remote_code=True,
+)
+# Code completion example
+prompt = '''def fibonacci(n: int) -> int:
+    """Return the n-th Fibonacci number."""
+'''
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=256,
+    temperature=0.2,
+    do_sample=True,
+)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))

uv.lock CHANGED Viewed

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