| --- |
| license: bsd-3-clause |
| datasets: |
| - pedrodev2026/microcoder-dataset-1024-tokens |
| base_model: |
| - unsloth/Qwen2.5-Coder-1.5B-Instruct |
| pipeline_tag: text-generation |
| tags: |
| - coder |
| - code |
| - microcoder |
| --- |
| # Microcoder 1.5B |
|
|
| **Microcoder 1.5B** is a code-focused language model fine-tuned from [Qwen 2.5 Coder 1.5B Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) using LoRA (Low-Rank Adaptation) on curated code datasets. It is designed for code generation, completion, and instruction-following tasks in a lightweight, efficient package. |
|
|
| --- |
|
|
| ## Model Details |
|
|
| | Property | Value | |
| |------------------|--------------------------------------------| |
| | **Base Model** | Qwen 2.5 Coder 1.5B Instruct | |
| | **Fine-tuning** | LoRA | |
| | **Parameters** | ~1.5B | |
| | **License** | BSD 3-Clause | |
| | **Language** | English (primary), multilingual code | |
| | **Task** | Code generation, completion, instruction following | |
|
|
| --- |
|
|
| ## Benchmarks |
|
|
| | Benchmark | Metric | Score | |
| |--------------------|----------|--------------| |
| | HumanEval | pass@1 | **59.15%** | |
| | MBPP+ | pass@1 | **52.91%** | |
| > HumanEval and MBPP+ results were obtained using the model in **GGUF format** with **Q5_K_M quantization**. Results may vary slightly with other formats or quantization levels. |
|
|
| --- |
|
|
| ## Usage |
|
|
| > **Important:** You must use `apply_chat_template` when formatting inputs. Passing raw text directly to the tokenizer will produce incorrect results. |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| |
| model_id = "your-org/microcoder-1.5b" |
| |
| tokenizer = AutoTokenizer.from_pretrained(model_id) |
| model = AutoModelForCausalLM.from_pretrained(model_id) |
| |
| messages = [ |
| { |
| "role": "user", |
| "content": "Write a Python function that returns the nth Fibonacci number." |
| } |
| ] |
| |
| input_text = tokenizer.apply_chat_template( |
| messages, |
| tokenize=False, |
| add_generation_prompt=True |
| ) |
| |
| inputs = tokenizer(input_text, return_tensors="pt") |
| outputs = model.generate(**inputs, max_new_tokens=256) |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
| ``` |
|
|
| --- |
|
|
| ## Training Details |
|
|
| Microcoder 1.5B was fine-tuned using LoRA on top of Qwen 2.5 Coder 1.5B Instruct. The training focused on code-heavy datasets covering multiple programming languages and problem-solving scenarios, aiming to improve instruction-following and code correctness at a small model scale. |
|
|
| --- |
|
|
| ## Credits |
|
|
| - **Model credits** — see [`MODEL_CREDITS.md`](./MODEL_CREDITS.md) |
| - **Dataset credits** — see [`DATASET_CREDITS.md`](./DATASET_CREDITS.md) |
|
|
| --- |
|
|
| ## License |
|
|
| The Microcoder 1.5B model weights and associated code in this repository are released under the **BSD 3-Clause License**. See [`LICENSE`](./LICENSE) for details. |
|
|
| Note that the base model (Qwen 2.5 Coder 1.5B Instruct) and the datasets used for fine-tuning are subject to their own respective licenses, as detailed in the credit files above. |
|
|
| --- |
|
|
| ## Notice |
|
|
| The documentation files in this repository (including `README.md`, `MODEL_CREDITS.md`, `DATASET_CREDITS.md`, and other `.md` files) were generated with the assistance of an AI language model. |
