---
datasets:
- bigcode/the-stack
- bigcode/the-stack-v2
- bigcode/starcoderdata
- bigcode/commitpack

---
Llama-Coyote.Coder-4B (GGUF)

📌 Model Overview

Model Name: WithinUsAI/Llama-Coyote.Coder-4B.gguf
Organization: Within Us AI
Model Type: Code LLM (Instruction-Tuned, Agentic-Oriented)
Parameter Size: 4B
Format: GGUF (quantized for local inference)
Primary Focus: Efficient coding + reasoning for local deployment

This model is part of the Within Us AI ecosystem of compact, high-performance coding models, designed to run locally while still delivering structured reasoning and practical software engineering output.  

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🧬 Architecture & Lineage

* Base Family: LLaMA-derived architecture (inferred from naming and ecosystem patterns)
* Model Class: Dense transformer (~4B parameters)
* Optimization Strategy:
    * Instruction tuning for coding tasks
    * Reasoning-aware outputs
    * GGUF quantization for edge deployment

Ecosystem Position

This model sits alongside:

* Other 4B coding models
* Agentic coders
* Reasoning-distilled systems

WithinUsAI focuses on agentic AI, tool use, and evaluation-driven training pipelines.  

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🧠 Core Design Philosophy

Think of this model like a desert-hardened code hunter 🐺💻

Lean, efficient, and tuned to track down solutions without wasting compute.

Design Goals:

* Maximize coding performance per parameter
* Encourage structured, step-by-step reasoning
* Enable local-first AI development
* Support agent-style workflows

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⚙️ Key Capabilities

💻 Coding

* Multi-language support (Python, JS, C++, etc.)
* Function generation and refactoring
* Debugging assistance
* Algorithm design

🤖 Agentic Behavior

* Task decomposition
* Instruction-following
* Compatible with tool-calling frameworks

🧠 Reasoning

* Step-by-step logic chains
* Problem breakdown
* Lightweight analytical reasoning

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📦 GGUF Format & Deployment

Optimized for local inference environments:

Supported Runtimes:

* llama.cpp
* LM Studio
* Ollama (GGUF-compatible builds)

Typical Quantization Options (4B):

Quant	RAM Needed	Notes
Q4_K_M	~3–4 GB	Best balance
Q5_K_M	~4–5 GB	Higher quality
Q8_0	~6–8 GB	Maximum fidelity

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🚀 Intended Use

✅ Ideal Use Cases

* Local coding assistants
* AI-powered IDE integrations
* Autonomous coding agents
* Script generation & debugging
* Offline development workflows

⚠️ Limitations

* Smaller parameter size limits deep reasoning vs larger models
* Performance depends on prompt clarity
* Tool use requires external orchestration

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🛠️ Usage Example (llama.cpp)

./main -m Llama-Coyote.Coder-4B.Q4_K_M.gguf \
  -p "Write a Python script that monitors file changes and logs them." \
  -n 512

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🧪 Training & Methodology

Within Us AI training approach includes:

* Code-focused instruction tuning
* Reasoning trace exposure
* Evaluation-driven dataset design
* Agentic workflow alignment

Data Sources

* Proprietary datasets created by Within Us AI
* Third-party datasets used without ownership claims
* Focus on:
    * Code reasoning
    * Debugging patterns
    * Structured outputs

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📊 Expected Performance Profile

Capability	Strength
Coding	High
Efficiency	Very High
Reasoning depth	Moderate
General knowledge	Moderate
Agent readiness	High

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📜 License

License Type: Custom / Other (Within Us AI License Approach)**

Terms:

* Base architecture derived from third-party LLM ecosystems (e.g., LLaMA family)
* Within Us AI developed:
    * Fine-tuning process
    * Model merging techniques
    * Training methodology
* Third-party datasets may be used without ownership claims
* Credit belongs to original creators

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🙏 Acknowledgements

* Meta (LLaMA architecture inspiration)
* Open-source GGUF / llama.cpp ecosystem
* Hugging Face community
* Dataset creators and contributors

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🔗 Links

* Model: https://huggingface.co/WithinUsAI/Llama-Coyote.Coder-4B.gguf
* Organization: https://huggingface.co/WithinUsAI

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🧩 Closing Note

This one feels like a quiet operator in the sand 🏜️

Not loud. Not oversized.
Just tracks the problem… and delivers code that works.