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