Create README.md

README.md

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+---
+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 Gemma 3 4B 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!