| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - code |
| - coding |
| - programming |
| - reasoning |
| - small-model |
| - efficient |
| - local |
| - qwen |
| - qwen3 |
| - qwen3.5 |
| - 4b |
| - small |
| - developer |
| - coding-assistant |
| - python |
| - debugging |
| - daily-use |
| - localai |
| - ai |
| - gpt |
| - dqnlabs |
| - dqngpt |
| - gguf |
| - lmstudio |
| - ollama |
| pipeline_tag: text-generation |
| --- |
| |
| # dqnCode-v1 |
|
|
| dqnCode-v1 is a 4B-parameter language model designed for fast, clear, and practical coding assistance. |
|
|
| It focuses on writing, fixing, and explaining code efficiently, with minimal verbosity and strong real-world usefulness. It is optimized for everyday programming tasks with low latency and concise outputs. |
|
|
|  |
| --- |
|
|
| ## Benchmark |
|
|
| dqnCode-v1 is positioned as a high-performance compact coding model, with strong results on standard code generation benchmarks. It is trained with simple prompts in mind, so you don't need to be a developer to use it! |
|
|
| ### HumanEval |
|
|
| - **pass@1:** 63.4% |
|
|
| This score places dqnCode-v1 among the strongest models in the 4B parameter class for coding tasks (only beaten by one other model in the 4B or below models class!) |
|
|
| | Model | Provider | HumanEval (pass@1) | |
| |--------------------------|-----------------|--------------------| |
| | GPT-3.5 Turbo | OpenAI | 68% | |
| | GPT-4 | OpenAI | 67% | |
| | dqnCode v1 (4B) | DQN Labs | 63.4% | |
| | Phi-3.5-mini-instruct | Microsoft | 62.8% | |
| | DeepSeek Coder 33B | DeepSeek | 52.4% | |
| | Gemma 2 27B | Google | 51.8% | |
| | Nous Hermes 3 405B | Nous Research | 51.4% | |
| --- |
|
|
| ## Benchmark Context |
|
|
| - Evaluated on HumanEval (Python code generation benchmark) |
| - Focused on functional correctness of generated code |
| - Designed to reflect real-world coding performance in a compact model |
|
|
| --- |
|
|
| ## Positioning |
|
|
| dqnCode-v1 is designed to deliver coding performance comparable to significantly larger models while maintaining a small footprint. |
|
|
| - Approaches GPT-3.5 / GPT-4-level performance on many practical coding tasks |
| - Achieves strong performance with significantly fewer parameters |
| - Optimized for fast local inference and real-world development workflows |
| - Excellent at all languages, especially HTML-CSS-JS, Python, Java, and more! |
|
|
|
|
| ## Model Description |
|
|
| - **Model type:** Causal Language Model |
| - **Parameters:** 4B |
| - **Primary use:** Coding and programming assistance |
| - **Style:** Direct code generation with minimal explanation |
|
|
| dqnCode-v1 4B is optimized for practical coding workflows rather than long-form development or benchmark performance. |
|
|
| --- |
|
|
| ## Intended Uses |
|
|
| ### Direct Use |
|
|
| - Writing code snippets |
| - Debugging errors |
| - Explaining code logic |
| - Converting code between languages |
| - Assisting with small to medium programming tasks |
| - Competitive programming support |
|
|
| --- |
|
|
| ## Key Characteristics |
|
|
| - Produces clean and functional code |
| - Prioritizes correctness and simplicity |
| - Minimizes unnecessary explanation |
| - Strong at debugging and fixing issues |
| - Designed for consistent outputs across common coding tasks |
| - Excellent instruction following and stability. |
| - Trained with simple prompts in mind, so you don't need to be a developer to use it! |
|
|
| --- |
|
|
| ## Example |
|
|
| **Input** |
| ```text |
| Write a Python function to check if a number is prime |
| ``` |
|
|
| **Output** |
| ```python |
| def is_prime(n): |
| if n < 2: |
| return False |
| for i in range(2, int(n**0.5) + 1): |
| if n % i == 0: |
| return False |
| return True |
| ``` |
|
|
| --- |
|
|
| **Input** |
| ```text |
| Fix this code: |
| for i in range(5) |
| print(i) |
| ``` |
|
|
| **Output** |
| ```python |
| for i in range(5): |
| print(i) |
| ``` |
|
|
| --- |
|
|
| ## Usage |
|
|
| This model is available on many platforms and is compatible with many formats. |
|
|
| The GGUF format is compatible with llama.cpp and LM Studio. |
| Other formats include MLX (LM Studio, optimized for Apple devices), and HF (universal compatibility). |
|
|
| --- |
|
|
| ## Training Details |
|
|
| dqnCode-v1 is fine-tuned for practical coding tasks and efficient problem solving. |
|
|
| The training process emphasizes: |
|
|
| - Functional correctness |
| - Minimal and clean outputs |
| - Real-world coding scenarios |
| - Debugging and code repair |
|
|
| --- |
|
|
| ## Limitations |
|
|
| - Limited performance on very large or complex codebases |
| - Not optimized for long-form software architecture design |
| - May simplify explanations rather than provide deep theoretical detail |
|
|
| --- |
|
|
| ## Efficiency |
|
|
| dqnCode-v1 is designed to run efficiently on consumer hardware, with support for quantized formats. |
|
|
| --- |
|
|
| ## License |
|
|
| Apache 2.0 |
|
|
| --- |
|
|
| ## Author |
|
|
| Developed by DQN Labs. |
| This model card was generated with the help of dqnGPT v0.2! |
