Darwin-Astral-4B-Coder

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.

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.

Model Summary

This model is intended for:

• code generation
• code explanation
• debugging assistance
• implementation planning
• instruction-following
• developer assistant workflows
• local or hosted coding inference

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.

Base Model Lineage

The current repository metadata lists the following upstream model references:

• LucidityAI/Astral-4B-Coder
• openfree/Darwin-Qwen3-4B
• Qwen/Qwen3-4B

The visible merge configuration in the README also shows:

• Qwen/Qwen3-4B-Instruct-2507 as the base model in the YAML block
• Lucidity-AI-Astral-4B-Coder as a merge source
• openfree-Darwin-Qwen3-4B as a merge source

These names are preserved here as shown on the repository page.

Merge Details

According to the current README:

• this model is a merge of pre-trained language models
• it was created using mergekit
• the SLERP merge method was used

The repository also includes a visible mergekit_config.yml, which supports the merge-based packaging of the release.

Dataset Lineage

The repository page currently shows the following dataset association:

• LucidityAI/Astral-Post-Training-Dataset

This suggests coding or post-training lineage connected to the Astral family used in the merge.

Intended Use

Recommended use cases include:

• coding assistant experiments
• generating utility functions and scripts
• explaining code and technical concepts
• debugging support
• step-by-step implementation planning
• local developer tools
• hosted text-generation workflows for software tasks

Suggested Use Cases

This model can be useful for:

• drafting Python, JavaScript, or general-purpose code
• proposing refactors
• generating boilerplate
• answering developer questions
• comparing implementation approaches
• producing structured technical responses

Out-of-Scope Use

This model should not be relied on for:

• legal advice
• medical advice
• financial advice
• safety-critical automation
• autonomous production engineering without review
• security-critical code without expert validation

All generated code should be reviewed, tested, and validated before real-world deployment.

Repository Contents

The repository currently includes standard Hugging Face model assets such as:

• README.md
• .gitattributes
• added_tokens.json
• config.json
• mergekit_config.yml
• merges.txt
• model-00001-of-00002.safetensors
• model-00002-of-00002.safetensors
• model.safetensors.index.json
• special_tokens_map.json
• tokenizer.json
• tokenizer_config.json

Prompting Guidance

This model will usually work best with prompts that are:

• direct
• scoped to a clear task
• explicit about the language or framework
• clear about whether code, explanation, or both are wanted
• structured when step-by-step reasoning is useful

Example prompt styles

Code generation

Write a Python function that loads a JSON file, validates required keys, and returns cleaned records.

Debugging

Explain why this code raises a KeyError and provide a safer corrected version.

Implementation planning

Create a step-by-step plan for building a FastAPI service with authentication, logging, and tests.

Refactoring

Refactor this function for readability and add basic error handling.

Strengths

This model may be especially useful for:

• blended coding workflows
• practical developer assistance
• moderate-size local inference
• structured software-task prompting
• merge-model experimentation
• compact coder deployments

Limitations

Like other merged 4B-class language models, this model may:

• hallucinate APIs or implementation details
• generate incomplete or incorrect code
• produce insecure patterns
• make reasoning mistakes on harder prompts
• require prompt iteration for best results
• need human validation before real-world use

Attribution

WithIn Us AI is the publisher of this merged model release.

Credit for upstream assets remains with their original creators. The repository metadata and README specifically reference:

• LucidityAI/Astral-4B-Coder
• openfree/Darwin-Qwen3-4B
• Qwen/Qwen3-4B
• Qwen/Qwen3-4B-Instruct-2507

and the dataset:

• LucidityAI/Astral-Post-Training-Dataset

License

This draft uses:

• license: other

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.

Acknowledgments

Thanks to:

• WithIn Us AI
• LucidityAI
• openfree
• Qwen
• the mergekit ecosystem
• the Hugging Face platform
• the broader open-source LLM community

Disclaimer

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.