README.md

---
license: other
library_name: transformers
base_model:
  - LucidityAI/Astral-4B-Coder
  - openfree/Darwin-Qwen3-4B
  - Qwen/Qwen3-4B
tags:
  - qwen3
  - mergekit
  - merge
  - text-generation-inference
  - code
  - coder
  - withinusai
language:
  - en
datasets:
  - LucidityAI/Astral-Post-Training-Dataset
pipeline_tag: text-generation
---

# 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.