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Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.","Heer-Patel69\u002FAetheris","Heer-Patel69","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F225642641?v=4","01M77YA5MD24QFSR03S12N4VK5","Heer Dipakbhai  Patel ","01M77YA5N2Z9P54X25VFPFMZJP",1,"MIT","https:\u002F\u002Fgithub.com\u002FHeer-Patel69\u002FAetheris\u002Fblob\u002Fmain\u002Fthird party\u002Fclaude-code-templates-main\u002Fclaude-code-templates-main\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Foptimization-gguf\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: gguf-quantization\ndescription: GGUF format and llama.cpp quantization for efficient CPU\u002FGPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [GGUF, Quantization, llama.cpp, CPU Inference, Apple Silicon, Model Compression, Optimization]\ndependencies: [llama-cpp-python>=0.2.0]\n---\n\n# GGUF - Quantization Format for llama.cpp\n\nThe GGUF (GPT-Generated Unified Format) is the standard file format for llama.cpp, enabling efficient inference on CPUs, Apple Silicon, and GPUs with flexible quantization options.\n\n## When to use GGUF\n\n**Use GGUF when:**\n- Deploying on consumer hardware (laptops, desktops)\n- Running on Apple Silicon (M1\u002FM2\u002FM3) with Metal acceleration\n- Need CPU inference without GPU requirements\n- Want flexible quantization (Q2_K to Q8_0)\n- Using local AI tools (LM Studio, Ollama, text-generation-webui)\n\n**Key advantages:**\n- **Universal hardware**: CPU, Apple Silicon, NVIDIA, AMD support\n- **No Python runtime**: Pure C\u002FC++ inference\n- **Flexible quantization**: 2-8 bit with various methods (K-quants)\n- **Ecosystem support**: LM Studio, Ollama, koboldcpp, and more\n- **imatrix**: Importance matrix for better low-bit quality\n\n**Use alternatives instead:**\n- **AWQ\u002FGPTQ**: Maximum accuracy with calibration on NVIDIA GPUs\n- **HQQ**: Fast calibration-free quantization for HuggingFace\n- **bitsandbytes**: Simple integration with transformers library\n- **TensorRT-LLM**: Production NVIDIA deployment with maximum speed\n\n## Quick start\n\n### Installation\n\n```bash\n# Clone llama.cpp\ngit clone https:\u002F\u002Fgithub.com\u002Fggml-org\u002Fllama.cpp\ncd llama.cpp\n\n# Build (CPU)\nmake\n\n# Build with CUDA (NVIDIA)\nmake GGML_CUDA=1\n\n# Build with Metal (Apple Silicon)\nmake GGML_METAL=1\n\n# Install Python bindings (optional)\npip install llama-cpp-python\n```\n\n### Convert model to GGUF\n\n```bash\n# Install requirements\npip install -r requirements.txt\n\n# Convert HuggingFace model to GGUF (FP16)\npython convert_hf_to_gguf.py .\u002Fpath\u002Fto\u002Fmodel --outfile model-f16.gguf\n\n# Or specify output type\npython convert_hf_to_gguf.py .\u002Fpath\u002Fto\u002Fmodel \\\n    --outfile model-f16.gguf \\\n    --outtype f16\n```\n\n### Quantize model\n\n```bash\n# Basic quantization to Q4_K_M\n.\u002Fllama-quantize model-f16.gguf model-q4_k_m.gguf Q4_K_M\n\n# Quantize with importance matrix (better quality)\n.\u002Fllama-imatrix -m model-f16.gguf -f calibration.txt -o model.imatrix\n.\u002Fllama-quantize --imatrix model.imatrix model-f16.gguf model-q4_k_m.gguf Q4_K_M\n```\n\n### Run inference\n\n```bash\n# CLI inference\n.\u002Fllama-cli -m model-q4_k_m.gguf -p \"Hello, how are you?\"\n\n# Interactive mode\n.\u002Fllama-cli -m model-q4_k_m.gguf --interactive\n\n# With GPU offload\n.\u002Fllama-cli -m model-q4_k_m.gguf -ngl 35 -p \"Hello!\"\n```\n\n## Quantization types\n\n### K-quant methods (recommended)\n\n| Type | Bits | Size (7B) | Quality | Use Case |\n|------|------|-----------|---------|----------|\n| Q2_K | 2.5 | ~2.8 GB | Low | Extreme compression |\n| Q3_K_S | 3.0 | ~3.0 GB | Low-Med | Memory constrained |\n| Q3_K_M | 3.3 | ~3.3 GB | Medium | Balance |\n| Q4_K_S | 4.0 | ~3.8 GB | Med-High | Good balance |\n| Q4_K_M | 4.5 | ~4.1 GB | High | **Recommended default** |\n| Q5_K_S | 5.0 | ~4.6 GB | High | Quality focused |\n| Q5_K_M | 5.5 | ~4.8 GB | Very High | High quality |\n| Q6_K | 6.0 | ~5.5 GB | Excellent | Near-original |\n| Q8_0 | 8.0 | ~7.2 GB | Best | Maximum quality |\n\n### Legacy methods\n\n| Type | Description |\n|------|-------------|\n| Q4_0 | 4-bit, basic |\n| Q4_1 | 4-bit with delta |\n| Q5_0 | 5-bit, basic |\n| Q5_1 | 5-bit with delta |\n\n**Recommendation**: Use K-quant methods (Q4_K_M, Q5_K_M) for best quality\u002Fsize ratio.\n\n## Conversion workflows\n\n### Workflow 1: HuggingFace to GGUF\n\n```bash\n# 1. Download model\nhuggingface-cli download meta-llama\u002FLlama-3.1-8B --local-dir .\u002Fllama-3.1-8b\n\n# 2. Convert to GGUF (FP16)\npython convert_hf_to_gguf.py .\u002Fllama-3.1-8b \\\n    --outfile llama-3.1-8b-f16.gguf \\\n    --outtype f16\n\n# 3. Quantize\n.\u002Fllama-quantize llama-3.1-8b-f16.gguf llama-3.1-8b-q4_k_m.gguf Q4_K_M\n\n# 4. Test\n.\u002Fllama-cli -m llama-3.1-8b-q4_k_m.gguf -p \"Hello!\" -n 50\n```\n\n### Workflow 2: With importance matrix (better quality)\n\n```bash\n# 1. Convert to GGUF\npython convert_hf_to_gguf.py .\u002Fmodel --outfile model-f16.gguf\n\n# 2. Create calibration text (diverse samples)\ncat > calibration.txt \u003C\u003C 'EOF'\nThe quick brown fox jumps over the lazy dog.\nMachine learning is a subset of artificial intelligence.\nPython is a popular programming language.\n# Add more diverse text samples...\nEOF\n\n# 3. Generate importance matrix\n.\u002Fllama-imatrix -m model-f16.gguf \\\n    -f calibration.txt \\\n    --chunk 512 \\\n    -o model.imatrix \\\n    -ngl 35  # GPU layers if available\n\n# 4. Quantize with imatrix\n.\u002Fllama-quantize --imatrix model.imatrix \\\n    model-f16.gguf \\\n    model-q4_k_m.gguf \\\n    Q4_K_M\n```\n\n### Workflow 3: Multiple quantizations\n\n```bash\n#!\u002Fbin\u002Fbash\nMODEL=\"llama-3.1-8b-f16.gguf\"\nIMATRIX=\"llama-3.1-8b.imatrix\"\n\n# Generate imatrix once\n.\u002Fllama-imatrix -m $MODEL -f wiki.txt -o $IMATRIX -ngl 35\n\n# Create multiple quantizations\nfor QUANT in Q4_K_M Q5_K_M Q6_K Q8_0; do\n    OUTPUT=\"llama-3.1-8b-${QUANT,,}.gguf\"\n    .\u002Fllama-quantize --imatrix $IMATRIX $MODEL $OUTPUT $QUANT\n    echo \"Created: $OUTPUT ($(du -h $OUTPUT | cut -f1))\"\ndone\n```\n\n## Python usage\n\n### llama-cpp-python\n\n```python\nfrom llama_cpp import Llama\n\n# Load model\nllm = Llama(\n    model_path=\".\u002Fmodel-q4_k_m.gguf\",\n    n_ctx=4096,          # Context window\n    n_gpu_layers=35,     # GPU offload (0 for CPU only)\n    n_threads=8          # CPU threads\n)\n\n# Generate\noutput = llm(\n    \"What is machine learning?\",\n    max_tokens=256,\n    temperature=0.7,\n    stop=[\"\u003C\u002Fs>\", \"\\n\\n\"]\n)\nprint(output[\"choices\"][0][\"text\"])\n```\n\n### Chat completion\n\n```python\nfrom llama_cpp import Llama\n\nllm = Llama(\n    model_path=\".\u002Fmodel-q4_k_m.gguf\",\n    n_ctx=4096,\n    n_gpu_layers=35,\n    chat_format=\"llama-3\"  # Or \"chatml\", \"mistral\", etc.\n)\n\nmessages = [\n    {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n    {\"role\": \"user\", \"content\": \"What is Python?\"}\n]\n\nresponse = llm.create_chat_completion(\n    messages=messages,\n    max_tokens=256,\n    temperature=0.7\n)\nprint(response[\"choices\"][0][\"message\"][\"content\"])\n```\n\n### Streaming\n\n```python\nfrom llama_cpp import Llama\n\nllm = Llama(model_path=\".\u002Fmodel-q4_k_m.gguf\", n_gpu_layers=35)\n\n# Stream tokens\nfor chunk in llm(\n    \"Explain quantum computing:\",\n    max_tokens=256,\n    stream=True\n):\n    print(chunk[\"choices\"][0][\"text\"], end=\"\", flush=True)\n```\n\n## Server mode\n\n### Start OpenAI-compatible server\n\n```bash\n# Start server\n.\u002Fllama-server -m model-q4_k_m.gguf \\\n    --host 0.0.0.0 \\\n    --port 8080 \\\n    -ngl 35 \\\n    -c 4096\n\n# Or with Python bindings\npython -m llama_cpp.server \\\n    --model model-q4_k_m.gguf \\\n    --n_gpu_layers 35 \\\n    --host 0.0.0.0 \\\n    --port 8080\n```\n\n### Use with OpenAI client\n\n```python\nfrom openai import OpenAI\n\nclient = OpenAI(\n    base_url=\"http:\u002F\u002Flocalhost:8080\u002Fv1\",\n    api_key=\"not-needed\"\n)\n\nresponse = client.chat.completions.create(\n    model=\"local-model\",\n    messages=[{\"role\": \"user\", \"content\": \"Hello!\"}],\n    max_tokens=256\n)\nprint(response.choices[0].message.content)\n```\n\n## Hardware optimization\n\n### Apple Silicon (Metal)\n\n```bash\n# Build with Metal\nmake clean && make GGML_METAL=1\n\n# Run with Metal acceleration\n.\u002Fllama-cli -m model.gguf -ngl 99 -p \"Hello\"\n\n# Python with Metal\nllm = Llama(\n    model_path=\"model.gguf\",\n    n_gpu_layers=99,     # Offload all layers\n    n_threads=1          # Metal handles parallelism\n)\n```\n\n### NVIDIA CUDA\n\n```bash\n# Build with CUDA\nmake clean && make GGML_CUDA=1\n\n# Run with CUDA\n.\u002Fllama-cli -m model.gguf -ngl 35 -p \"Hello\"\n\n# Specify GPU\nCUDA_VISIBLE_DEVICES=0 .\u002Fllama-cli -m model.gguf -ngl 35\n```\n\n### CPU optimization\n\n```bash\n# Build with AVX2\u002FAVX512\nmake clean && make\n\n# Run with optimal threads\n.\u002Fllama-cli -m model.gguf -t 8 -p \"Hello\"\n\n# Python CPU config\nllm = Llama(\n    model_path=\"model.gguf\",\n    n_gpu_layers=0,      # CPU only\n    n_threads=8,         # Match physical cores\n    n_batch=512          # Batch size for prompt processing\n)\n```\n\n## Integration with tools\n\n### Ollama\n\n```bash\n# Create Modelfile\ncat > Modelfile \u003C\u003C 'EOF'\nFROM .\u002Fmodel-q4_k_m.gguf\nTEMPLATE \"\"\"{{ .System }}\n{{ .Prompt }}\"\"\"\nPARAMETER temperature 0.7\nPARAMETER num_ctx 4096\nEOF\n\n# Create Ollama model\nollama create mymodel -f Modelfile\n\n# Run\nollama run mymodel \"Hello!\"\n```\n\n### LM Studio\n\n1. Place GGUF file in `~\u002F.cache\u002Flm-studio\u002Fmodels\u002F`\n2. Open LM Studio and select the model\n3. Configure context length and GPU offload\n4. Start inference\n\n### text-generation-webui\n\n```bash\n# Place in models folder\ncp model-q4_k_m.gguf text-generation-webui\u002Fmodels\u002F\n\n# Start with llama.cpp loader\npython server.py --model model-q4_k_m.gguf --loader llama.cpp --n-gpu-layers 35\n```\n\n## Best practices\n\n1. **Use K-quants**: Q4_K_M offers best quality\u002Fsize balance\n2. **Use imatrix**: Always use importance matrix for Q4 and below\n3. **GPU offload**: Offload as many layers as VRAM allows\n4. **Context length**: Start with 4096, increase if needed\n5. **Thread count**: Match physical CPU cores, not logical\n6. **Batch size**: Increase n_batch for faster prompt processing\n\n## Common issues\n\n**Model loads slowly:**\n```bash\n# Use mmap for faster loading\n.\u002Fllama-cli -m model.gguf --mmap\n```\n\n**Out of memory:**\n```bash\n# Reduce GPU layers\n.\u002Fllama-cli -m model.gguf -ngl 20  # Reduce from 35\n\n# Or use smaller quantization\n.\u002Fllama-quantize model-f16.gguf model-q3_k_m.gguf Q3_K_M\n```\n\n**Poor quality at low bits:**\n```bash\n# Always use imatrix for Q4 and below\n.\u002Fllama-imatrix -m model-f16.gguf -f calibration.txt -o model.imatrix\n.\u002Fllama-quantize --imatrix model.imatrix model-f16.gguf model-q4_k_m.gguf Q4_K_M\n```\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - Batching, speculative decoding, custom builds\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues, debugging, benchmarks\n\n## Resources\n\n- **Repository**: https:\u002F\u002Fgithub.com\u002Fggml-org\u002Fllama.cpp\n- **Python Bindings**: https:\u002F\u002Fgithub.com\u002Fabetlen\u002Fllama-cpp-python\n- **Pre-quantized Models**: https:\u002F\u002Fhuggingface.co\u002FTheBloke\n- **GGUF Converter**: https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fggml-org\u002Fgguf-my-repo\n- **License**: MIT\n","# GGUF - Quantization Format for llama.cpp\n\nThe GGUF (GPT-Generated Unified Format) is the standard file format for llama.cpp, enabling efficient inference on CPUs, Apple Silicon, and GPUs with flexible quantization options.\n\n## When to use GGUF\n\n**Use GGUF when:**\n- Deploying on consumer hardware (laptops, desktops)\n- Running on Apple Silicon (M1\u002FM2\u002FM3) with Metal acceleration\n- Need CPU inference without GPU requirements\n- Want flexible quantization (Q2_K to Q8_0)\n- Using local AI tools (LM Studio, Ollama, text-generation-webui)\n\n**Key advantages:**\n- **Universal hardware**: CPU, Apple Silicon, NVIDIA, AMD support\n- **No Python runtime**: Pure C\u002FC++ inference\n- **Flexible quantization**: 2-8 bit with various methods (K-quants)\n- **Ecosystem support**: LM Studio, Ollama, koboldcpp, and more\n- **imatrix**: Importance matrix for better low-bit quality\n\n**Use alternatives instead:**\n- **AWQ\u002FGPTQ**: Maximum accuracy with calibration on NVIDIA GPUs\n- **HQQ**: Fast calibration-free quantization for HuggingFace\n- **bitsandbytes**: Simple integration with transformers library\n- **TensorRT-LLM**: Production NVIDIA deployment with maximum speed\n\n## Quick start\n\n### Installation\n\n```bash\n# Clone llama.cpp\ngit clone https:\u002F\u002Fgithub.com\u002Fggml-org\u002Fllama.cpp\ncd llama.cpp\n\n# Build (CPU)\nmake\n\n# Build with CUDA (NVIDIA)\nmake GGML_CUDA=1\n\n# Build with Metal (Apple Silicon)\nmake GGML_METAL=1\n\n# Install Python bindings (optional)\npip install llama-cpp-python\n```\n\n### Convert model to GGUF\n\n```bash\n# Install requirements\npip install -r requirements.txt\n\n# Convert HuggingFace model to GGUF (FP16)\npython convert_hf_to_gguf.py .\u002Fpath\u002Fto\u002Fmodel --outfile model-f16.gguf\n\n# Or specify output type\npython convert_hf_to_gguf.py .\u002Fpath\u002Fto\u002Fmodel \\\n    --outfile model-f16.gguf \\\n    --outtype f16\n```\n\n### Quantize model\n\n```bash\n# Basic quantization to Q4_K_M\n.\u002Fllama-quantize model-f16.gguf model-q4_k_m.gguf Q4_K_M\n\n# Quantize with importance matrix (better quality)\n.\u002Fllama-imatrix -m model-f16.gguf -f calibration.txt -o model.imatrix\n.\u002Fllama-quantize --imatrix model.imatrix model-f16.gguf model-q4_k_m.gguf Q4_K_M\n```\n\n### Run inference\n\n```bash\n# CLI inference\n.\u002Fllama-cli -m model-q4_k_m.gguf -p \"Hello, how are you?\"\n\n# Interactive mode\n.\u002Fllama-cli -m model-q4_k_m.gguf --interactive\n\n# With GPU offload\n.\u002Fllama-cli -m model-q4_k_m.gguf -ngl 35 -p \"Hello!\"\n```\n\n## Quantization types\n\n### K-quant methods (recommended)\n\n| Type | Bits | Size (7B) | Quality | Use Case |\n|------|------|-----------|---------|----------|\n| Q2_K | 2.5 | ~2.8 GB | Low | Extreme compression |\n| Q3_K_S | 3.0 | ~3.0 GB | Low-Med | Memory constrained |\n| Q3_K_M | 3.3 | ~3.3 GB | Medium | Balance |\n| Q4_K_S | 4.0 | ~3.8 GB | Med-High | Good balance |\n| Q4_K_M | 4.5 | ~4.1 GB | High | **Recommended default** |\n| Q5_K_S | 5.0 | ~4.6 GB | High | Quality focused |\n| Q5_K_M | 5.5 | ~4.8 GB | Very High | High quality |\n| Q6_K | 6.0 | ~5.5 GB | Excellent | Near-original |\n| Q8_0 | 8.0 | ~7.2 GB | Best | Maximum quality |\n\n### Legacy methods\n\n| Type | Description |\n|------|-------------|\n| Q4_0 | 4-bit, basic |\n| Q4_1 | 4-bit with delta |\n| Q5_0 | 5-bit, basic |\n| Q5_1 | 5-bit with delta |\n\n**Recommendation**: Use K-quant methods (Q4_K_M, Q5_K_M) for best quality\u002Fsize ratio.\n\n## Conversion workflows\n\n### Workflow 1: HuggingFace to GGUF\n\n```bash\n# 1. Download model\nhuggingface-cli download meta-llama\u002FLlama-3.1-8B --local-dir .\u002Fllama-3.1-8b\n\n# 2. Convert to GGUF (FP16)\npython convert_hf_to_gguf.py .\u002Fllama-3.1-8b \\\n    --outfile llama-3.1-8b-f16.gguf \\\n    --outtype f16\n\n# 3. Quantize\n.\u002Fllama-quantize llama-3.1-8b-f16.gguf llama-3.1-8b-q4_k_m.gguf Q4_K_M\n\n# 4. Test\n.\u002Fllama-cli -m llama-3.1-8b-q4_k_m.gguf -p \"Hello!\" -n 50\n```\n\n### Workflow 2: With importance matrix (better quality)\n\n```bash\n# 1. Convert to GGUF\npython convert_hf_to_gguf.py .\u002Fmodel --outfile model-f16.gguf\n\n# 2. Create calibration text (diverse samples)\ncat > calibration.txt \u003C\u003C 'EOF'\nThe quick brown fox jumps over the lazy dog.\nMachine learning is a subset of artificial intelligence.\nPython is a popular programming language.\n# Add more diverse text samples...\nEOF\n\n# 3. Generate importance matrix\n.\u002Fllama-imatrix -m model-f16.gguf \\\n    -f calibration.txt \\\n    --chunk 512 \\\n    -o model.imatrix \\\n    -ngl 35  # GPU layers if available\n\n# 4. Quantize with imatrix\n.\u002Fllama-quantize --imatrix model.imatrix \\\n    model-f16.gguf \\\n    model-q4_k_m.gguf \\\n    Q4_K_M\n```\n\n### Workflow 3: Multiple quantizations\n\n```bash\n#!\u002Fbin\u002Fbash\nMODEL=\"llama-3.1-8b-f16.gguf\"\nIMATRIX=\"llama-3.1-8b.imatrix\"\n\n# Generate imatrix once\n.\u002Fllama-imatrix -m $MODEL -f wiki.txt -o $IMATRIX -ngl 35\n\n# Create multiple quantizations\nfor QUANT in Q4_K_M Q5_K_M Q6_K Q8_0; do\n    OUTPUT=\"llama-3.1-8b-${QUANT,,}.gguf\"\n    .\u002Fllama-quantize --imatrix $IMATRIX $MODEL $OUTPUT $QUANT\n    echo \"Created: $OUTPUT ($(du -h $OUTPUT | cut -f1))\"\ndone\n```\n\n## Python usage\n\n### llama-cpp-python\n\n```python\nfrom llama_cpp import Llama\n\n# Load model\nllm = Llama(\n    model_path=\".\u002Fmodel-q4_k_m.gguf\",\n    n_ctx=4096,          # Context window\n    n_gpu_layers=35,     # GPU offload (0 for CPU only)\n    n_threads=8          # CPU threads\n)\n\n# Generate\noutput = llm(\n    \"What is machine learning?\",\n    max_tokens=256,\n    temperature=0.7,\n    stop=[\"\u003C\u002Fs>\", \"\\n\\n\"]\n)\nprint(output[\"choices\"][0][\"text\"])\n```\n\n### Chat completion\n\n```python\nfrom llama_cpp import Llama\n\nllm = Llama(\n    model_path=\".\u002Fmodel-q4_k_m.gguf\",\n    n_ctx=4096,\n    n_gpu_layers=35,\n    chat_format=\"llama-3\"  # Or \"chatml\", \"mistral\", etc.\n)\n\nmessages = [\n    {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n    {\"role\": \"user\", \"content\": \"What is Python?\"}\n]\n\nresponse = llm.create_chat_completion(\n    messages=messages,\n    max_tokens=256,\n    temperature=0.7\n)\nprint(response[\"choices\"][0][\"message\"][\"content\"])\n```\n\n### Streaming\n\n```python\nfrom llama_cpp import Llama\n\nllm = Llama(model_path=\".\u002Fmodel-q4_k_m.gguf\", n_gpu_layers=35)\n\n# Stream tokens\nfor chunk in llm(\n    \"Explain quantum computing:\",\n    max_tokens=256,\n    stream=True\n):\n    print(chunk[\"choices\"][0][\"text\"], end=\"\", flush=True)\n```\n\n## Server mode\n\n### Start OpenAI-compatible server\n\n```bash\n# Start server\n.\u002Fllama-server -m model-q4_k_m.gguf \\\n    --host 0.0.0.0 \\\n    --port 8080 \\\n    -ngl 35 \\\n    -c 4096\n\n# Or with Python bindings\npython -m llama_cpp.server \\\n    --model model-q4_k_m.gguf \\\n    --n_gpu_layers 35 \\\n    --host 0.0.0.0 \\\n    --port 8080\n```\n\n### Use with OpenAI client\n\n```python\nfrom openai import OpenAI\n\nclient = OpenAI(\n    base_url=\"http:\u002F\u002Flocalhost:8080\u002Fv1\",\n    api_key=\"not-needed\"\n)\n\nresponse = client.chat.completions.create(\n    model=\"local-model\",\n    messages=[{\"role\": \"user\", \"content\": \"Hello!\"}],\n    max_tokens=256\n)\nprint(response.choices[0].message.content)\n```\n\n## Hardware optimization\n\n### Apple Silicon (Metal)\n\n```bash\n# Build with Metal\nmake clean && make GGML_METAL=1\n\n# Run with Metal acceleration\n.\u002Fllama-cli -m model.gguf -ngl 99 -p \"Hello\"\n\n# Python with Metal\nllm = Llama(\n    model_path=\"model.gguf\",\n    n_gpu_layers=99,     # Offload all layers\n    n_threads=1          # Metal handles parallelism\n)\n```\n\n### NVIDIA CUDA\n\n```bash\n# Build with CUDA\nmake clean && make GGML_CUDA=1\n\n# Run with CUDA\n.\u002Fllama-cli -m model.gguf -ngl 35 -p \"Hello\"\n\n# Specify GPU\nCUDA_VISIBLE_DEVICES=0 .\u002Fllama-cli -m model.gguf -ngl 35\n```\n\n### CPU optimization\n\n```bash\n# Build with AVX2\u002FAVX512\nmake clean && make\n\n# Run with optimal threads\n.\u002Fllama-cli -m model.gguf -t 8 -p \"Hello\"\n\n# Python CPU config\nllm = Llama(\n    model_path=\"model.gguf\",\n    n_gpu_layers=0,      # CPU only\n    n_threads=8,         # Match physical cores\n    n_batch=512          # Batch size for prompt processing\n)\n```\n\n## Integration with tools\n\n### Ollama\n\n```bash\n# Create Modelfile\ncat > Modelfile \u003C\u003C 'EOF'\nFROM .\u002Fmodel-q4_k_m.gguf\nTEMPLATE \"\"\"{{ .System }}\n{{ .Prompt }}\"\"\"\nPARAMETER temperature 0.7\nPARAMETER num_ctx 4096\nEOF\n\n# Create Ollama model\nollama create mymodel -f Modelfile\n\n# Run\nollama run mymodel \"Hello!\"\n```\n\n### LM Studio\n\n1. Place GGUF file in `~\u002F.cache\u002Flm-studio\u002Fmodels\u002F`\n2. Open LM Studio and select the model\n3. Configure context length and GPU offload\n4. Start inference\n\n### text-generation-webui\n\n```bash\n# Place in models folder\ncp model-q4_k_m.gguf text-generation-webui\u002Fmodels\u002F\n\n# Start with llama.cpp loader\npython server.py --model model-q4_k_m.gguf --loader llama.cpp --n-gpu-layers 35\n```\n\n## Best practices\n\n1. **Use K-quants**: Q4_K_M offers best quality\u002Fsize balance\n2. **Use imatrix**: Always use importance matrix for Q4 and below\n3. **GPU offload**: Offload as many layers as VRAM allows\n4. **Context length**: Start with 4096, increase if needed\n5. **Thread count**: Match physical CPU cores, not logical\n6. **Batch size**: Increase n_batch for faster prompt processing\n\n## Common issues\n\n**Model loads slowly:**\n```bash\n# Use mmap for faster loading\n.\u002Fllama-cli -m model.gguf --mmap\n```\n\n**Out of memory:**\n```bash\n# Reduce GPU layers\n.\u002Fllama-cli -m model.gguf -ngl 20  # Reduce from 35\n\n# Or use smaller quantization\n.\u002Fllama-quantize model-f16.gguf model-q3_k_m.gguf Q3_K_M\n```\n\n**Poor quality at low bits:**\n```bash\n# Always use imatrix for Q4 and below\n.\u002Fllama-imatrix -m model-f16.gguf -f calibration.txt -o model.imatrix\n.\u002Fllama-quantize --imatrix model.imatrix model-f16.gguf model-q4_k_m.gguf Q4_K_M\n```\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - Batching, speculative decoding, custom builds\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues, debugging, benchmarks\n\n## Resources\n\n- **Repository**: https:\u002F\u002Fgithub.com\u002Fggml-org\u002Fllama.cpp\n- **Python Bindings**: https:\u002F\u002Fgithub.com\u002Fabetlen\u002Fllama-cpp-python\n- **Pre-quantized Models**: https:\u002F\u002Fhuggingface.co\u002FTheBloke\n- **GGUF Converter**: https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fggml-org\u002Fgguf-my-repo\n- **License**: MIT\n",{"name":5,"tags":24,"author":32,"license":15,"version":33,"description":7,"dependencies":34},[25,26,27,28,29,30,31],"GGUF","Quantization","llama.cpp","CPU Inference","Apple Silicon","Model Compression","Optimization","Orchestra Research","1.0.0",[35],"llama-cpp-python>=0.2.0","main","0edefbe9101b21ed9b961f914c13ae42b268e2a4","third party\u002Fclaude-code-templates-main\u002Fclaude-code-templates-main\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Foptimization-gguf",0,"https:\u002F\u002Fgithub.com\u002FHeer-Patel69\u002FAetheris","https:\u002F\u002Fgithub.com\u002FHeer-Patel69","2026-07-19 01:50:57",{"source":8,"skillName":5,"commands":44,"prompt":54,"zipAvailable":55},[45,48,51],{"tool":46,"command":47},"npx","npx skills add Heer-Patel69\u002FAetheris --skill gguf-quantization",{"tool":49,"command":50},"bunx","bunx skills add Heer-Patel69\u002FAetheris --skill gguf-quantization",{"tool":52,"command":53},"pnpm","pnpm dlx skills add Heer-Patel69\u002FAetheris --skill gguf-quantization","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FHeer-Patel69\u002FAetheris\u002Fblob\u002Fmain\u002Fthird party\u002Fclaude-code-templates-main\u002Fclaude-code-templates-main\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Foptimization-gguf\u002FSKILL.md",true,[57,63],{"path":58,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":61,"sourceHtmlUrl":62},"references\u002Fadvanced-usage.md","REFERENCE","参考文档",10887,"https:\u002F\u002Fgithub.com\u002FHeer-Patel69\u002FAetheris\u002Fblob\u002Fmain\u002Fthird party\u002Fclaude-code-templates-main\u002Fclaude-code-templates-main\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Foptimization-gguf\u002Freferences\u002Fadvanced-usage.md",{"path":64,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":65,"sourceHtmlUrl":66},"references\u002Ftroubleshooting.md",8904,"https:\u002F\u002Fgithub.com\u002FHeer-Patel69\u002FAetheris\u002Fblob\u002Fmain\u002Fthird party\u002Fclaude-code-templates-main\u002Fclaude-code-templates-main\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Foptimization-gguf\u002Freferences\u002Ftroubleshooting.md",[68,77,84,91,98,105],{"skillNo":69,"name":70,"nameZh":6,"description":71,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":72,"licenseFileUrl":73,"sourceHtmlUrl":74,"categories":75,"keywords":76,"publishedAt":19},"01M786K8RTQF44DN3R4DQRNHH0","yeet","Use only when the user explicitly asks to stage, commit, push, and open a GitHub pull request in one flow using the GitHub CLI (`gh`).","Apache-2.0","https:\u002F\u002Fgithub.com\u002FHeer-Patel69\u002FAetheris\u002Fblob\u002Fmain\u002Fthird party\u002Fclaude-code-templates-main\u002Fclaude-code-templates-main\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fyeet\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FHeer-Patel69\u002FAetheris\u002Fblob\u002Fmain\u002Fthird party\u002Fclaude-code-templates-main\u002Fclaude-code-templates-main\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fyeet\u002FSKILL.md",[],[],{"skillNo":78,"name":79,"nameZh":6,"description":80,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":81,"categories":82,"keywords":83,"publishedAt":19},"01M786FRBBC04CFM75BAVZK2DG","dependabot-review","Review and manage Dependabot PRs. 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No exceptions.\n\nUSE FOR: Any URL, webpage, web search, \"scrape\", \"search the web\", \"get data from\", \"look up\", \"find online\", \"research\", structured data from Amazon\u002FLinkedIn\u002FInstagram\u002FTikTok\u002FYouTube\u002FFacebook\u002FX\u002FReddit, browser automation, e-commerce, social media monitoring, lead generation, reading docs\u002Farticles\u002Fsites, current events, fact-checking.\n\nReturns clean markdown or structured JSON. Handles JavaScript, CAPTCHAs, bot detection bypass. 60+ tools. Always use Bright Data MCP for any internet task. 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