refactor: reorganize skills into sub-categories

The skills directory was getting disorganized — mlops alone had 40 skills in a flat list, and 12 categories were singletons with just one skill each. Code change: - prompt_builder.py: Support sub-categories in skill scanner. skills/mlops/training/axolotl/SKILL.md now shows as category 'mlops/training' instead of just 'mlops'. Backwards-compatible with existing flat structure. Split mlops (40 skills) into 7 sub-categories: - mlops/training (12): accelerate, axolotl, flash-attention, grpo-rl-training, peft, pytorch-fsdp, pytorch-lightning, simpo, slime, torchtitan, trl-fine-tuning, unsloth - mlops/inference (8): gguf, guidance, instructor, llama-cpp, obliteratus, outlines, tensorrt-llm, vllm - mlops/models (6): audiocraft, clip, llava, segment-anything, stable-diffusion, whisper - mlops/vector-databases (4): chroma, faiss, pinecone, qdrant - mlops/evaluation (5): huggingface-tokenizers, lm-evaluation-harness, nemo-curator, saelens, weights-and-biases - mlops/cloud (2): lambda-labs, modal - mlops/research (1): dspy Merged singleton categories: - gifs → media (gif-search joins youtube-content) - music-creation → media (heartmula, songsee) - diagramming → creative (excalidraw joins ascii-art) - ocr-and-documents → productivity - domain → research (domain-intel) - feeds → research (blogwatcher) - market-data → research (polymarket) Fixed misplaced skills: - mlops/code-review → software-development (not ML-specific) - mlops/ml-paper-writing → research (academic writing) Added DESCRIPTION.md files for all new/updated categories.
2026-03-09 03:35:53 -07:00
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--- a/skills/mlops/inference/llama-cpp/SKILL.md
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+---
+name: llama-cpp
+description: Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
+version: 1.0.0
+author: Orchestra Research
+license: MIT
+dependencies: [llama-cpp-python]
+metadata:
+  hermes:
+    tags: [Inference Serving, Llama.cpp, CPU Inference, Apple Silicon, Edge Deployment, GGUF, Quantization, Non-NVIDIA, AMD GPUs, Intel GPUs, Embedded]
+
+---
+
+# llama.cpp
+
+Pure C/C++ LLM inference with minimal dependencies, optimized for CPUs and non-NVIDIA hardware.
+
+## When to use llama.cpp
+
+**Use llama.cpp when:**
+- Running on CPU-only machines
+- Deploying on Apple Silicon (M1/M2/M3/M4)
+- Using AMD or Intel GPUs (no CUDA)
+- Edge deployment (Raspberry Pi, embedded systems)
+- Need simple deployment without Docker/Python
+
+**Use TensorRT-LLM instead when:**
+- Have NVIDIA GPUs (A100/H100)
+- Need maximum throughput (100K+ tok/s)
+- Running in datacenter with CUDA
+
+**Use vLLM instead when:**
+- Have NVIDIA GPUs
+- Need Python-first API
+- Want PagedAttention
+
+## Quick start
+
+### Installation
+
+```bash
+# macOS/Linux
+brew install llama.cpp
+
+# Or build from source
+git clone https://github.com/ggerganov/llama.cpp
+cd llama.cpp
+make
+
+# With Metal (Apple Silicon)
+make LLAMA_METAL=1
+
+# With CUDA (NVIDIA)
+make LLAMA_CUDA=1
+
+# With ROCm (AMD)
+make LLAMA_HIP=1
+```
+
+### Download model
+
+```bash
+# Download from HuggingFace (GGUF format)
+huggingface-cli download \
+    TheBloke/Llama-2-7B-Chat-GGUF \
+    llama-2-7b-chat.Q4_K_M.gguf \
+    --local-dir models/
+
+# Or convert from HuggingFace
+python convert_hf_to_gguf.py models/llama-2-7b-chat/
+```
+
+### Run inference
+
+```bash
+# Simple chat
+./llama-cli \
+    -m models/llama-2-7b-chat.Q4_K_M.gguf \
+    -p "Explain quantum computing" \
+    -n 256  # Max tokens
+
+# Interactive chat
+./llama-cli \
+    -m models/llama-2-7b-chat.Q4_K_M.gguf \
+    --interactive
+```
+
+### Server mode
+
+```bash
+# Start OpenAI-compatible server
+./llama-server \
+    -m models/llama-2-7b-chat.Q4_K_M.gguf \
+    --host 0.0.0.0 \
+    --port 8080 \
+    -ngl 32  # Offload 32 layers to GPU
+
+# Client request
+curl http://localhost:8080/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "llama-2-7b-chat",
+    "messages": [{"role": "user", "content": "Hello!"}],
+    "temperature": 0.7,
+    "max_tokens": 100
+  }'
+```
+
+## Quantization formats
+
+### GGUF format overview
+
+| Format | Bits | Size (7B) | Speed | Quality | Use Case |
+|--------|------|-----------|-------|---------|----------|
+| **Q4_K_M** | 4.5 | 4.1 GB | Fast | Good | **Recommended default** |
+| Q4_K_S | 4.3 | 3.9 GB | Faster | Lower | Speed critical |
+| Q5_K_M | 5.5 | 4.8 GB | Medium | Better | Quality critical |
+| Q6_K | 6.5 | 5.5 GB | Slower | Best | Maximum quality |
+| Q8_0 | 8.0 | 7.0 GB | Slow | Excellent | Minimal degradation |
+| Q2_K | 2.5 | 2.7 GB | Fastest | Poor | Testing only |
+
+### Choosing quantization
+
+```bash
+# General use (balanced)
+Q4_K_M  # 4-bit, medium quality
+
+# Maximum speed (more degradation)
+Q2_K or Q3_K_M
+
+# Maximum quality (slower)
+Q6_K or Q8_0
+
+# Very large models (70B, 405B)
+Q3_K_M or Q4_K_S  # Lower bits to fit in memory
+```
+
+## Hardware acceleration
+
+### Apple Silicon (Metal)
+
+```bash
+# Build with Metal
+make LLAMA_METAL=1
+
+# Run with GPU acceleration (automatic)
+./llama-cli -m model.gguf -ngl 999  # Offload all layers
+
+# Performance: M3 Max 40-60 tokens/sec (Llama 2-7B Q4_K_M)
+```
+
+### NVIDIA GPUs (CUDA)
+
+```bash
+# Build with CUDA
+make LLAMA_CUDA=1
+
+# Offload layers to GPU
+./llama-cli -m model.gguf -ngl 35  # Offload 35/40 layers
+
+# Hybrid CPU+GPU for large models
+./llama-cli -m llama-70b.Q4_K_M.gguf -ngl 20  # GPU: 20 layers, CPU: rest
+```
+
+### AMD GPUs (ROCm)
+
+```bash
+# Build with ROCm
+make LLAMA_HIP=1
+
+# Run with AMD GPU
+./llama-cli -m model.gguf -ngl 999
+```
+
+## Common patterns
+
+### Batch processing
+
+```bash
+# Process multiple prompts from file
+cat prompts.txt | ./llama-cli \
+    -m model.gguf \
+    --batch-size 512 \
+    -n 100
+```
+
+### Constrained generation
+
+```bash
+# JSON output with grammar
+./llama-cli \
+    -m model.gguf \
+    -p "Generate a person: " \
+    --grammar-file grammars/json.gbnf
+
+# Outputs valid JSON only
+```
+
+### Context size
+
+```bash
+# Increase context (default 512)
+./llama-cli \
+    -m model.gguf \
+    -c 4096  # 4K context window
+
+# Very long context (if model supports)
+./llama-cli -m model.gguf -c 32768  # 32K context
+```
+
+## Performance benchmarks
+
+### CPU performance (Llama 2-7B Q4_K_M)
+
+| CPU | Threads | Speed | Cost |
+|-----|---------|-------|------|
+| Apple M3 Max | 16 | 50 tok/s | $0 (local) |
+| AMD Ryzen 9 7950X | 32 | 35 tok/s | $0.50/hour |
+| Intel i9-13900K | 32 | 30 tok/s | $0.40/hour |
+| AWS c7i.16xlarge | 64 | 40 tok/s | $2.88/hour |
+
+### GPU acceleration (Llama 2-7B Q4_K_M)
+
+| GPU | Speed | vs CPU | Cost |
+|-----|-------|--------|------|
+| NVIDIA RTX 4090 | 120 tok/s | 3-4× | $0 (local) |
+| NVIDIA A10 | 80 tok/s | 2-3× | $1.00/hour |
+| AMD MI250 | 70 tok/s | 2× | $2.00/hour |
+| Apple M3 Max (Metal) | 50 tok/s | ~Same | $0 (local) |
+
+## Supported models
+
+**LLaMA family**:
+- Llama 2 (7B, 13B, 70B)
+- Llama 3 (8B, 70B, 405B)
+- Code Llama
+
+**Mistral family**:
+- Mistral 7B
+- Mixtral 8x7B, 8x22B
+
+**Other**:
+- Falcon, BLOOM, GPT-J
+- Phi-3, Gemma, Qwen
+- LLaVA (vision), Whisper (audio)
+
+**Find models**: https://huggingface.co/models?library=gguf
+
+## References
+
+- **[Quantization Guide](references/quantization.md)** - GGUF formats, conversion, quality comparison
+- **[Server Deployment](references/server.md)** - API endpoints, Docker, monitoring
+- **[Optimization](references/optimization.md)** - Performance tuning, hybrid CPU+GPU
+
+## Resources
+
+- **GitHub**: https://github.com/ggerganov/llama.cpp
+- **Models**: https://huggingface.co/models?library=gguf
+- **Discord**: https://discord.gg/llama-cpp
+
+