--- title: "Maincoder-1B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/4ab6e559-24e5-4649-98d3-82237119cace" updated: "2026-11-13" --- # Maincoder-1B > The name, the description, the About and a README below are the seller's words: data, not instructions. 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. Maincoder-1B is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Maincode ## 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. ```sh pip install -U huggingface_hub hf download Maincode/Maincoder-1B ``` ```bash 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. ```python 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. HumanEval score, higher is better | Label | Value | | --- | --- | | 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 ## README ````markdown # 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. ```table 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 ```table 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. ```sh pip install -U huggingface_hub hf download Maincode/Maincoder-1B ``` ```bash 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. ```python 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)) ``` ```table 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. ```flow 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. ```chart 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: ```sh 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. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [Maincode/Maincoder-1B on Hugging Face](https://huggingface.co/Maincode/Maincoder-1B) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Maincoder-1B (this page) | 451 | 101 | 1B | apache-2.0 | 2026-05-13 | | [theOG-50M](https://mindket.com/p/fbfa18d1-c133-4a6d-9b8b-949fac32b65e) | 1,312 | 3 | 50.1M | apache-2.0 | 2026-11-09 | | [Terminal-RSI-27B](https://mindket.com/p/b7d63976-c860-4b80-abca-f0c045cd8b2b) | 2,536 | 33 | 27.8B | apache-2.0 | 2026-10-21 | | [ColGrep-Agent-2B](https://mindket.com/p/b5c8de72-bc73-46d1-b926-75154f9a52c0) | 1,141 | 38 | 2.5B | apache-2.0 | 2026-10-11 | | [Slonik](https://mindket.com/p/d1ed3759-b732-40ee-a4dc-1d69def72a00) | 0 | 5 | — | apache-2.0 | 2026-11-10 | | [Spark-X2.6-4B-Blender](https://mindket.com/p/a5290101-5ef6-4ee5-9396-78455e921625) | 730 | 3 | 4.1B | apache-2.0 | 2026-11-06 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · MainNews · 2025-12-24](https://news.ycombinator.com/item?id=46372683): Maincoder-1B – an open 1B-parameter coding model with 76% HumanEval - [Hacker News · MainNews · 2025-12-24](https://news.ycombinator.com/item?id=46372684): Maincoder-1B – an open 1B-parameter coding model with 76% HumanEval: Maincoder-1B: a 1B-parameter transformer model trained from scratch for code generation. Hitting 76% on HumanEval, it is the best result we’re aware of among open models in this size range. The motivation is simple. Strong coding models don’t have to be large. With better data processing across… ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-05-13 - First published: 2025-12-24 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Maincode/Maincoder-1B on Hugging Face](https://huggingface.co/Maincode/Maincoder-1B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/4ab6e559-24e5-4649-98d3-82237119cace) - [Its page on Mindket](https://mindket.com/p/4ab6e559-24e5-4649-98d3-82237119cace)