Maincoder-1B

Maincoder-1B

Completes and writes Python code from a function signature or a docstring. A 1B code model from Maincode that is small enough to run on consumer hardware, for developers who want local code completion.

Made by Maincode on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.

About

What it is: Maincoder-1B is a 1 billion parameter language model from Maincode, tuned for code generation and completion, with a focus on Python. It is small enough to run on a consumer computer. Its training ends with a reinforcement learning stage that the authors name MCPO.

Who it suits: Developers who want a compact code model on their own machine, and researchers who compare small coding models. The context window is short, at 2,048 tokens, so it suits single functions and classes more than whole files.

Industry | Who uses it | How they use it | Example
Software | Developers | Complete functions from a signature and docstring | Finishing a quicksort function from its description
Education | Programming teachers | Show code completion in a classroom with no cloud | A live demo of writing a binary search tree
Tooling | Editor plugin builders | Use a small local model for completions | An offline completion helper
Research | Code model researchers | Compare small models on coding benchmarks | Checking scores on HumanEval and MBPP+

You can skip it if you need long-context work or general knowledge answers, since its scores on general tests are modest and its context is short.

Where to use it:

• Python scripts that use the transformers library
• Windows, macOS or Linux with Python installed
• Your own computer, since the model is small enough for consumer hardware

Use case | Who | What they get back
Function completion | Developers | A finished function body from a signature and docstring
Class completion | Developers | A class skeleton continued from its start
Algorithm drafts | Learners | A first version of a known algorithm

How to set it up:

• Python on Windows, macOS or Linux
• The huggingface_hub package, then transformers and torch
• Room for about 2.1 GB of weights in BF16, or about 513 MB at 4-bit by Hugging Face's estimate

1. Install the Hugging Face client.
2. Download the model files to your computer.
3. Install transformers and torch.
4. Load the model and tokenizer, then give it a code prompt.

pip install -U huggingface_hub
hf download Maincode/Maincoder-1B

pip install transformers torch

How to use it: Start the prompt with a function signature and a docstring, and let the model write the body. A low temperature keeps the output steady. The same approach works for classes and for algorithm tasks.

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Maincode/Maincoder-1B",
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
    "Maincode/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))

Tool | What it does
AutoModelForCausalLM | Loads the model with its custom code
AutoTokenizer | Turns your prompt into tokens and back
model.generate | Writes the continuation, here up to 256 new tokens
temperature | Set to 0.2 in the example to keep output steady

Example: You give the model a function signature for a quicksort and a one-line docstring. It writes the function body that follows. Read the result and run your own tests before you rely on it.

For newcomers and experts: If you are new, run the Fibonacci snippet above first to confirm everything loads. Then change the signature and docstring to your own function.

If you know the field: the model is a 32-layer decoder with rotary embeddings, grouped-query attention at a 4 to 1 ratio, QK normalization and SwiGLU. Its hidden size is 1536, with 16 heads and a 151,936-token vocabulary.

Signature and docstring
Tokenizer
Maincoder-1B decoder
Code completion

Good to know: On HumanEval the authors report the model ahead of the small coding and general models they compared. Its general-knowledge scores are lower than some of them.

Title: HumanEval score, higher is better
Maincoder-1B: 0.7622 score
deepseek-coder-1.3b-instruct: 0.5610 score
SmolLM3-3B: 0.5366 score
Qwen2.5-Coder-1.5B-Instruct: 0.4634 score
Qwen3-1.7B: 0.4024 score

• Context: 2,048 tokens
• Language focus: Python, with English instructions
• License: Apache 2.0
• Cost to run: none named
• Data it reaches: model files downloaded to your computer, and the card's code uses trust_remote_code, which runs custom code from the repository
• Not for: long files or general-knowledge answers, since the context is short and general scores are lower

On Hugging Face, 13 November 2026

• Task: text generation
• Library: transformers
• Parameters: 1B
• Weights: about 2.1 GB in BF16; about 513 MB at 4-bit (estimate)
• Formats: Safetensors
• Downloads in the last 30 days: 451
• Likes: 101
• License: apache-2.0
• Languages: en
• Quantized versions: 7
• Tags: transformers, safetensors, maincoder, feature-extraction, code, python, code-generation, reinforcement-learning, mcpo, text-generation, conversational

Links

• Model: https://huggingface.co/Maincode/Maincoder-1B
• Files: https://huggingface.co/Maincode/Maincoder-1B/tree/main

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download Maincode/Maincoder-1B

Then follow the usage in its model card. Nothing in this kit runs by itself.