commit 7878db8edd5020ce2236c02f7f2846383043c1c3 Author: ModelHub XC Date: Wed Apr 22 06:59:46 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: DQN-Labs-Community/dqnCode-v1-GGUF Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..0fd7225 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,48 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text 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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. + +![dqnCode Banner](dqnCode.png) +--- + +## 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. +Huge thanks to the team at mradermacher for quantizing this model! +This model card was generated with the help of dqnGPT v0.2! diff --git a/dqnCode.png b/dqnCode.png new file mode 100644 index 0000000..f2ce310 --- /dev/null +++ b/dqnCode.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ab06ee64526465f09ad9ce601d909b3bab4dee57361f308e86cf041d54aca61a +size 610404