| --- |
| datasets: |
| - bigcode/the-stack |
| - bigcode/the-stack-v2 |
| - bigcode/starcoderdata |
| - bigcode/commitpack |
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| --- |
| Llama-Coyote.Coder-4B (GGUF) |
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| 📌 Model Overview |
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| 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 |
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| 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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| ⸻ |
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| 🧬 Architecture & Lineage |
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| * 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 |
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| Ecosystem Position |
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| This model sits alongside: |
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| * Other 4B coding models |
| * Agentic coders |
| * Reasoning-distilled systems |
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| WithinUsAI focuses on agentic AI, tool use, and evaluation-driven training pipelines.  |
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| 🧠 Core Design Philosophy |
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| Think of this model like a desert-hardened code hunter 🐺💻 |
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| Lean, efficient, and tuned to track down solutions without wasting compute. |
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| Design Goals: |
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| * Maximize coding performance per parameter |
| * Encourage structured, step-by-step reasoning |
| * Enable local-first AI development |
| * Support agent-style workflows |
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| ⸻ |
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| ⚙️ Key Capabilities |
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| 💻 Coding |
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| * Multi-language support (Python, JS, C++, etc.) |
| * Function generation and refactoring |
| * Debugging assistance |
| * Algorithm design |
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| 🤖 Agentic Behavior |
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| * Task decomposition |
| * Instruction-following |
| * Compatible with tool-calling frameworks |
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| 🧠 Reasoning |
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| * Step-by-step logic chains |
| * Problem breakdown |
| * Lightweight analytical reasoning |
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| ⸻ |
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| 📦 GGUF Format & Deployment |
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| Optimized for local inference environments: |
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| Supported Runtimes: |
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| * llama.cpp |
| * LM Studio |
| * Ollama (GGUF-compatible builds) |
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| Typical Quantization Options (4B): |
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| 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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| ⸻ |
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| 🚀 Intended Use |
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| ✅ Ideal Use Cases |
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| * Local coding assistants |
| * AI-powered IDE integrations |
| * Autonomous coding agents |
| * Script generation & debugging |
| * Offline development workflows |
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| ⚠️ Limitations |
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| * Smaller parameter size limits deep reasoning vs larger models |
| * Performance depends on prompt clarity |
| * Tool use requires external orchestration |
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| ⸻ |
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| 🛠️ Usage Example (llama.cpp) |
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| ./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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| ⸻ |
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| 🧪 Training & Methodology |
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| Within Us AI training approach includes: |
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| * Code-focused instruction tuning |
| * Reasoning trace exposure |
| * Evaluation-driven dataset design |
| * Agentic workflow alignment |
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| Data Sources |
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| * 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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| ⸻ |
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| 📊 Expected Performance Profile |
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| Capability Strength |
| Coding High |
| Efficiency Very High |
| Reasoning depth Moderate |
| General knowledge Moderate |
| Agent readiness High |
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| ⸻ |
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| 📜 License |
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| License Type: Custom / Other (Within Us AI License Approach)** |
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| Terms: |
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| * 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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| ⸻ |
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| 🙏 Acknowledgements |
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| * Meta (LLaMA architecture inspiration) |
| * Open-source GGUF / llama.cpp ecosystem |
| * Hugging Face community |
| * Dataset creators and contributors |
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| ⸻ |
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| 🔗 Links |
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| * Model: https://huggingface.co/WithinUsAI/Llama-Coyote.Coder-4B.gguf |
| * Organization: https://huggingface.co/WithinUsAI |
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| ⸻ |
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| 🧩 Closing Note |
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| This one feels like a quiet operator in the sand 🏜️ |
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| Not loud. Not oversized. |
| Just tracks the problem… and delivers code that works. |
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