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
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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.
