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Model: WithinUsAI/Darwin-Astral-Coder-4B Source: Original Platform
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README.md
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README.md
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license: other
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library_name: transformers
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base_model:
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- LucidityAI/Astral-4B-Coder
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- openfree/Darwin-Qwen3-4B
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- Qwen/Qwen3-4B
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tags:
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- qwen3
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- mergekit
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- merge
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- text-generation-inference
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- code
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- coder
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- withinusai
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language:
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- en
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datasets:
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- LucidityAI/Astral-Post-Training-Dataset
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pipeline_tag: text-generation
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---
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# Darwin-Astral-4B-Coder
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**Darwin-Astral-4B-Coder** is a merged 4B-class coding model release from **WithIn Us AI**, designed for code generation, instruction-following, and practical developer-assistant workflows.
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This repository is distributed as a standard **Transformers** checkpoint in **Safetensors** format and is positioned as a merge-based model that blends Darwin-style and Astral-style coding traits within a Qwen3-family 4B backbone.
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## Model Summary
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This model is intended for:
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- code generation
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- code explanation
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- debugging assistance
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- implementation planning
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- instruction-following
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- developer assistant workflows
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- local or hosted coding inference
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As a 4B-class model, it aims to balance stronger coding capability than very small models with a lighter deployment footprint than larger coder checkpoints.
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## Base Model Lineage
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The current repository metadata lists the following upstream model references:
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- `LucidityAI/Astral-4B-Coder`
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- `openfree/Darwin-Qwen3-4B`
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- `Qwen/Qwen3-4B`
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The visible merge configuration in the README also shows:
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- `Qwen/Qwen3-4B-Instruct-2507` as the base model in the YAML block
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- `Lucidity-AI-Astral-4B-Coder` as a merge source
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- `openfree-Darwin-Qwen3-4B` as a merge source
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These names are preserved here as shown on the repository page.
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## Merge Details
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According to the current README:
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- this model is a merge of pre-trained language models
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- it was created using **mergekit**
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- the **SLERP** merge method was used
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The repository also includes a visible `mergekit_config.yml`, which supports the merge-based packaging of the release.
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## Dataset Lineage
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The repository page currently shows the following dataset association:
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- `LucidityAI/Astral-Post-Training-Dataset`
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This suggests coding or post-training lineage connected to the Astral family used in the merge.
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## Intended Use
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Recommended use cases include:
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- coding assistant experiments
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- generating utility functions and scripts
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- explaining code and technical concepts
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- debugging support
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- step-by-step implementation planning
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- local developer tools
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- hosted text-generation workflows for software tasks
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## Suggested Use Cases
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This model can be useful for:
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- drafting Python, JavaScript, or general-purpose code
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- proposing refactors
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- generating boilerplate
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- answering developer questions
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- comparing implementation approaches
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- producing structured technical responses
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## Out-of-Scope Use
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This model should not be relied on for:
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- legal advice
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- medical advice
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- financial advice
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- safety-critical automation
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- autonomous production engineering without review
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- security-critical code without expert validation
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All generated code should be reviewed, tested, and validated before real-world deployment.
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## Repository Contents
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The repository currently includes standard Hugging Face model assets such as:
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- `README.md`
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- `.gitattributes`
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- `added_tokens.json`
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- `config.json`
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- `mergekit_config.yml`
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- `merges.txt`
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- `model-00001-of-00002.safetensors`
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- `model-00002-of-00002.safetensors`
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- `model.safetensors.index.json`
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- `special_tokens_map.json`
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- `tokenizer.json`
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- `tokenizer_config.json`
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## Prompting Guidance
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This model will usually work best with prompts that are:
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- direct
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- scoped to a clear task
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- explicit about the language or framework
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- clear about whether code, explanation, or both are wanted
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- structured when step-by-step reasoning is useful
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### Example prompt styles
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**Code generation**
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> Write a Python function that loads a JSON file, validates required keys, and returns cleaned records.
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**Debugging**
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> Explain why this code raises a KeyError and provide a safer corrected version.
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**Implementation planning**
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> Create a step-by-step plan for building a FastAPI service with authentication, logging, and tests.
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**Refactoring**
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> Refactor this function for readability and add basic error handling.
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## Strengths
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This model may be especially useful for:
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- blended coding workflows
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- practical developer assistance
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- moderate-size local inference
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- structured software-task prompting
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- merge-model experimentation
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- compact coder deployments
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## Limitations
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Like other merged 4B-class language models, this model may:
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- hallucinate APIs or implementation details
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- generate incomplete or incorrect code
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- produce insecure patterns
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- make reasoning mistakes on harder prompts
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- require prompt iteration for best results
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- need human validation before real-world use
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## Attribution
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**WithIn Us AI** is the publisher of this merged model release.
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Credit for upstream assets remains with their original creators. The repository metadata and README specifically reference:
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- `LucidityAI/Astral-4B-Coder`
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- `openfree/Darwin-Qwen3-4B`
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- `Qwen/Qwen3-4B`
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- `Qwen/Qwen3-4B-Instruct-2507`
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and the dataset:
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- `LucidityAI/Astral-Post-Training-Dataset`
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## License
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This draft uses:
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- `license: other`
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If you maintain this repo, replace this with the exact license terms you want displayed and make sure they align with any upstream obligations from the referenced source models and datasets.
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## Acknowledgments
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Thanks to:
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- **WithIn Us AI**
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- **LucidityAI**
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- **openfree**
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- **Qwen**
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- the **mergekit** ecosystem
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- the Hugging Face platform
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- the broader open-source LLM community
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## Disclaimer
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This model may produce inaccurate, insecure, biased, incomplete, or misleading outputs. All important generations, especially code and technical guidance, should be reviewed and tested before real-world use.
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