[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XG8NFK7DB41MHT0EA0GG2Q":3,"skill-same:01M6XG8NFK7DB41MHT0EA0GG2Q":83},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":53,"commitSha":54,"skillDirectory":55,"forksCount":14,"repositoryHtmlUrl":56,"repositoryHomepageUrl":6,"ownerHtmlUrl":57,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":58,"install":59,"resources":72},"01M6XG8NFK7DB41MHT0EA0GG2Q","awq-quantization",null,"Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.","fadrienne\u002FMyGitHub","fadrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","01M6X64V8VWCRSMFZ4EPS2BWXG","Francesca Adrienne","01M6X64VD5MW4F9PHDWQKS7ZAF",0,"MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fawq\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"awq quantization","Quantization","AWQ","4-Bit","when","2024","for","you","Marlin Kernels","vLLM Integration","Memory Optimization","Activation-Aware","memory","synthetic","bit","activation","Transformers","Fast Inference","multimodal","training","2026-11-10 10:12:30",false,"---\nname: awq-quantization\ndescription: Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.\ncategory: ml-training\nversion: 1.0.0\nauthor: Synthetic Sciences\nlicense: MIT\ntags: [Optimization, AWQ, Quantization, 4-Bit, Activation-Aware, Memory Optimization, Fast Inference, vLLM Integration, Marlin Kernels]\ndependencies: [autoawq, transformers>=4.45.0, torch>=2.0.0]\n---\n\n# AWQ (Activation-aware Weight Quantization)\n\n4-bit quantization that preserves salient weights based on activation patterns, achieving 3x speedup with minimal accuracy loss.\n\n## When to use AWQ\n\n**Use AWQ when:**\n- Need 4-bit quantization with \u003C5% accuracy loss\n- Deploying instruction-tuned or chat models (AWQ generalizes better)\n- Want ~2.5-3x inference speedup over FP16\n- Using vLLM for production serving\n- Have Ampere+ GPUs (A100, H100, RTX 40xx) for Marlin kernel support\n\n**Use GPTQ instead when:**\n- Need maximum ecosystem compatibility (more tools support GPTQ)\n- Working with ExLlamaV2 backend specifically\n- Have older GPUs without Marlin support\n\n**Use bitsandbytes instead when:**\n- Need zero calibration overhead (quantize on-the-fly)\n- Want to fine-tune with QLoRA\n- Prefer simpler integration\n\n## Quick start\n\n### Installation\n\n```bash\n# Default (Triton kernels)\npip install autoawq\n\n# With optimized CUDA kernels + Flash Attention\npip install autoawq[kernels]\n\n# Intel CPU\u002FXPU optimization\npip install autoawq[cpu]\n```\n\n**Requirements**: Python 3.8+, CUDA 11.8+, Compute Capability 7.5+\n\n### Load pre-quantized model\n\n```python\nfrom awq import AutoAWQForCausalLM\nfrom transformers import AutoTokenizer\n\nmodel_name = \"TheBloke\u002FMistral-7B-Instruct-v0.2-AWQ\"\n\nmodel = AutoAWQForCausalLM.from_quantized(\n    model_name,\n    fuse_layers=True  # Enable fused attention for speed\n)\ntokenizer = AutoTokenizer.from_pretrained(model_name)\n\n# Generate\ninputs = tokenizer(\"Explain quantum computing\", return_tensors=\"pt\").to(\"cuda\")\noutputs = model.generate(**inputs, max_new_tokens=200)\nprint(tokenizer.decode(outputs[0], skip_special_tokens=True))\n```\n\n### Quantize your own model\n\n```python\nfrom awq import AutoAWQForCausalLM\nfrom transformers import AutoTokenizer\n\nmodel_path = \"mistralai\u002FMistral-7B-Instruct-v0.2\"\n\n# Load model and tokenizer\nmodel = AutoAWQForCausalLM.from_pretrained(model_path)\ntokenizer = AutoTokenizer.from_pretrained(model_path)\n\n# Quantization config\nquant_config = {\n    \"zero_point\": True,      # Use zero-point quantization\n    \"q_group_size\": 128,     # Group size (128 recommended)\n    \"w_bit\": 4,              # 4-bit weights\n    \"version\": \"GEMM\"        # GEMM for batch, GEMV for single-token\n}\n\n# Quantize (uses pileval dataset by default)\nmodel.quantize(tokenizer, quant_config=quant_config)\n\n# Save\nmodel.save_quantized(\"mistral-7b-awq\")\ntokenizer.save_pretrained(\"mistral-7b-awq\")\n```\n\n**Timing**: ~10-15 min for 7B, ~1 hour for 70B models.\n\n## AWQ vs GPTQ vs bitsandbytes\n\n| Feature | AWQ | GPTQ | bitsandbytes |\n|---------|-----|------|--------------|\n| **Speedup (4-bit)** | ~2.5-3x | ~2x | ~1.5x |\n| **Accuracy loss** | \u003C5% | ~5-10% | ~5-15% |\n| **Calibration** | Minimal (128-1K tokens) | More extensive | None |\n| **Overfitting risk** | Low | Higher | N\u002FA |\n| **Best for** | Production inference | GPU inference | Easy integration |\n| **vLLM support** | Native | Yes | Limited |\n\n**Key insight**: AWQ assumes not all weights are equally important. It protects ~1% of salient weights identified by activation patterns, reducing quantization error without mixed-precision overhead.\n\n## Kernel backends\n\n### GEMM (default, batch inference)\n\n```python\nquant_config = {\n    \"zero_point\": True,\n    \"q_group_size\": 128,\n    \"w_bit\": 4,\n    \"version\": \"GEMM\"  # Best for batch sizes > 1\n}\n```\n\n### GEMV (single-token generation)\n\n```python\nquant_config = {\n    \"version\": \"GEMV\"  # 20% faster for batch_size=1\n}\n```\n\n**Limitation**: Only batch size 1, not good for large context.\n\n### Marlin (Ampere+ GPUs)\n\n```python\nfrom transformers import AwqConfig, AutoModelForCausalLM\n\nconfig = AwqConfig(\n    bits=4,\n    version=\"marlin\"  # 2x faster on A100\u002FH100\n)\n\nmodel = AutoModelForCausalLM.from_pretrained(\n    \"TheBloke\u002FMistral-7B-AWQ\",\n    quantization_config=config\n)\n```\n\n**Requirements**: Compute Capability 8.0+ (A100, H100, RTX 40xx)\n\n### ExLlamaV2 (AMD compatible)\n\n```python\nconfig = AwqConfig(\n    bits=4,\n    version=\"exllama\"  # Faster prefill, AMD GPU support\n)\n```\n\n## HuggingFace Transformers integration\n\n### Direct loading\n\n```python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\nmodel = AutoModelForCausalLM.from_pretrained(\n    \"TheBloke\u002Fzephyr-7B-alpha-AWQ\",\n    device_map=\"auto\"\n)\ntokenizer = AutoTokenizer.from_pretrained(\"TheBloke\u002Fzephyr-7B-alpha-AWQ\")\n```\n\n### Fused modules (recommended)\n\n```python\nfrom transformers import AwqConfig, AutoModelForCausalLM\n\nconfig = AwqConfig(\n    bits=4,\n    fuse_max_seq_len=512,  # Max sequence length for fusing\n    do_fuse=True           # Enable fused attention\u002FMLP\n)\n\nmodel = AutoModelForCausalLM.from_pretrained(\n    \"TheBloke\u002FMistral-7B-OpenOrca-AWQ\",\n    quantization_config=config\n)\n```\n\n**Note**: Fused modules cannot combine with FlashAttention2.\n\n## vLLM integration\n\n```python\nfrom vllm import LLM, SamplingParams\n\n# vLLM auto-detects AWQ models\nllm = LLM(\n    model=\"TheBloke\u002FLlama-2-7B-AWQ\",\n    quantization=\"awq\",\n    dtype=\"half\"\n)\n\nsampling = SamplingParams(temperature=0.7, max_tokens=200)\noutputs = llm.generate([\"Explain AI\"], sampling)\n```\n\n## Performance benchmarks\n\n### Memory reduction\n\n| Model | FP16 | AWQ 4-bit | Reduction |\n|-------|------|-----------|-----------|\n| Mistral 7B | 14 GB | 5.5 GB | 2.5x |\n| Llama 2-13B | 26 GB | 10 GB | 2.6x |\n| Llama 2-70B | 140 GB | 35 GB | 4x |\n\n### Inference speed (RTX 4090)\n\n| Model | Prefill (tok\u002Fs) | Decode (tok\u002Fs) | Memory |\n|-------|-----------------|----------------|--------|\n| Mistral 7B GEMM | 3,897 | 114 | 5.55 GB |\n| TinyLlama 1B GEMV | 5,179 | 431 | 2.10 GB |\n| Llama 2-13B GEMM | 2,279 | 74 | 10.28 GB |\n\n### Accuracy (perplexity)\n\n| Model | FP16 | AWQ 4-bit | Degradation |\n|-------|------|-----------|-------------|\n| Llama 3 8B | 8.20 | 8.48 | +3.4% |\n| Mistral 7B | 5.25 | 5.42 | +3.2% |\n| Qwen2 72B | 4.85 | 4.95 | +2.1% |\n\n## Custom calibration data\n\n```python\n# Use custom dataset for domain-specific models\nmodel.quantize(\n    tokenizer,\n    quant_config=quant_config,\n    calib_data=\"wikitext\",       # Or custom list of strings\n    max_calib_samples=256,       # More samples = better accuracy\n    max_calib_seq_len=512        # Sequence length\n)\n\n# Or provide your own samples\ncalib_samples = [\n    \"Your domain-specific text here...\",\n    \"More examples from your use case...\",\n]\nmodel.quantize(tokenizer, quant_config=quant_config, calib_data=calib_samples)\n```\n\n## Multi-GPU deployment\n\n```python\nmodel = AutoAWQForCausalLM.from_quantized(\n    \"TheBloke\u002FLlama-2-70B-AWQ\",\n    device_map=\"auto\",  # Auto-split across GPUs\n    max_memory={0: \"40GB\", 1: \"40GB\"}\n)\n```\n\n## Supported models\n\n35+ architectures including:\n- **Llama family**: Llama 2\u002F3, Code Llama, Mistral, Mixtral\n- **Qwen**: Qwen, Qwen2, Qwen2.5-VL\n- **Others**: Falcon, MPT, Phi, Yi, DeepSeek, Gemma\n- **Multimodal**: LLaVA, LLaVA-Next, Qwen2-VL\n\n## Common issues\n\n**CUDA OOM during quantization**:\n```python\n# Reduce batch size\nmodel.quantize(tokenizer, quant_config=quant_config, max_calib_samples=64)\n```\n\n**Slow inference**:\n```python\n# Enable fused layers\nmodel = AutoAWQForCausalLM.from_quantized(model_name, fuse_layers=True)\n```\n\n**AMD GPU support**:\n```python\n# Use ExLlama backend\nconfig = AwqConfig(bits=4, version=\"exllama\")\n```\n\n## Deprecation notice\n\nAutoAWQ is officially deprecated. For new projects, consider:\n- **vLLM llm-compressor**: https:\u002F\u002Fgithub.com\u002Fvllm-project\u002Fllm-compressor\n- **MLX-LM**: For Mac devices with Apple Silicon\n\nExisting quantized models remain usable.\n\n## References\n\n- **Paper**: AWQ: Activation-aware Weight Quantization (arXiv:2306.00978) - MLSys 2024 Best Paper\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fcasper-hansen\u002FAutoAWQ\n- **MIT Han Lab**: https:\u002F\u002Fgithub.com\u002Fmit-han-lab\u002Fllm-awq\n- **Models**: https:\u002F\u002Fhuggingface.co\u002Fmodels?library=awq\n","# AWQ (Activation-aware Weight Quantization)\n\n4-bit quantization that preserves salient weights based on activation patterns, achieving 3x speedup with minimal accuracy loss.\n\n## When to use AWQ\n\n**Use AWQ when:**\n- Need 4-bit quantization with \u003C5% accuracy loss\n- Deploying instruction-tuned or chat models (AWQ generalizes better)\n- Want ~2.5-3x inference speedup over FP16\n- Using vLLM for production serving\n- Have Ampere+ GPUs (A100, H100, RTX 40xx) for Marlin kernel support\n\n**Use GPTQ instead when:**\n- Need maximum ecosystem compatibility (more tools support GPTQ)\n- Working with ExLlamaV2 backend specifically\n- Have older GPUs without Marlin support\n\n**Use bitsandbytes instead when:**\n- Need zero calibration overhead (quantize on-the-fly)\n- Want to fine-tune with QLoRA\n- Prefer simpler integration\n\n## Quick start\n\n### Installation\n\n```bash\n# Default (Triton kernels)\npip install autoawq\n\n# With optimized CUDA kernels + Flash Attention\npip install autoawq[kernels]\n\n# Intel CPU\u002FXPU optimization\npip install autoawq[cpu]\n```\n\n**Requirements**: Python 3.8+, CUDA 11.8+, Compute Capability 7.5+\n\n### Load pre-quantized model\n\n```python\nfrom awq import AutoAWQForCausalLM\nfrom transformers import AutoTokenizer\n\nmodel_name = \"TheBloke\u002FMistral-7B-Instruct-v0.2-AWQ\"\n\nmodel = AutoAWQForCausalLM.from_quantized(\n    model_name,\n    fuse_layers=True  # Enable fused attention for speed\n)\ntokenizer = AutoTokenizer.from_pretrained(model_name)\n\n# Generate\ninputs = tokenizer(\"Explain quantum computing\", return_tensors=\"pt\").to(\"cuda\")\noutputs = model.generate(**inputs, max_new_tokens=200)\nprint(tokenizer.decode(outputs[0], skip_special_tokens=True))\n```\n\n### Quantize your own model\n\n```python\nfrom awq import AutoAWQForCausalLM\nfrom transformers import AutoTokenizer\n\nmodel_path = \"mistralai\u002FMistral-7B-Instruct-v0.2\"\n\n# Load model and tokenizer\nmodel = AutoAWQForCausalLM.from_pretrained(model_path)\ntokenizer = AutoTokenizer.from_pretrained(model_path)\n\n# Quantization config\nquant_config = {\n    \"zero_point\": True,      # Use zero-point quantization\n    \"q_group_size\": 128,     # Group size (128 recommended)\n    \"w_bit\": 4,              # 4-bit weights\n    \"version\": \"GEMM\"        # GEMM for batch, GEMV for single-token\n}\n\n# Quantize (uses pileval dataset by default)\nmodel.quantize(tokenizer, quant_config=quant_config)\n\n# Save\nmodel.save_quantized(\"mistral-7b-awq\")\ntokenizer.save_pretrained(\"mistral-7b-awq\")\n```\n\n**Timing**: ~10-15 min for 7B, ~1 hour for 70B models.\n\n## AWQ vs GPTQ vs bitsandbytes\n\n| Feature | AWQ | GPTQ | bitsandbytes |\n|---------|-----|------|--------------|\n| **Speedup (4-bit)** | ~2.5-3x | ~2x | ~1.5x |\n| **Accuracy loss** | \u003C5% | ~5-10% | ~5-15% |\n| **Calibration** | Minimal (128-1K tokens) | More extensive | None |\n| **Overfitting risk** | Low | Higher | N\u002FA |\n| **Best for** | Production inference | GPU inference | Easy integration |\n| **vLLM support** | Native | Yes | Limited |\n\n**Key insight**: AWQ assumes not all weights are equally important. It protects ~1% of salient weights identified by activation patterns, reducing quantization error without mixed-precision overhead.\n\n## Kernel backends\n\n### GEMM (default, batch inference)\n\n```python\nquant_config = {\n    \"zero_point\": True,\n    \"q_group_size\": 128,\n    \"w_bit\": 4,\n    \"version\": \"GEMM\"  # Best for batch sizes > 1\n}\n```\n\n### GEMV (single-token generation)\n\n```python\nquant_config = {\n    \"version\": \"GEMV\"  # 20% faster for batch_size=1\n}\n```\n\n**Limitation**: Only batch size 1, not good for large context.\n\n### Marlin (Ampere+ GPUs)\n\n```python\nfrom transformers import AwqConfig, AutoModelForCausalLM\n\nconfig = AwqConfig(\n    bits=4,\n    version=\"marlin\"  # 2x faster on A100\u002FH100\n)\n\nmodel = AutoModelForCausalLM.from_pretrained(\n    \"TheBloke\u002FMistral-7B-AWQ\",\n    quantization_config=config\n)\n```\n\n**Requirements**: Compute Capability 8.0+ (A100, H100, RTX 40xx)\n\n### ExLlamaV2 (AMD compatible)\n\n```python\nconfig = AwqConfig(\n    bits=4,\n    version=\"exllama\"  # Faster prefill, AMD GPU support\n)\n```\n\n## HuggingFace Transformers integration\n\n### Direct loading\n\n```python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\nmodel = AutoModelForCausalLM.from_pretrained(\n    \"TheBloke\u002Fzephyr-7B-alpha-AWQ\",\n    device_map=\"auto\"\n)\ntokenizer = AutoTokenizer.from_pretrained(\"TheBloke\u002Fzephyr-7B-alpha-AWQ\")\n```\n\n### Fused modules (recommended)\n\n```python\nfrom transformers import AwqConfig, AutoModelForCausalLM\n\nconfig = AwqConfig(\n    bits=4,\n    fuse_max_seq_len=512,  # Max sequence length for fusing\n    do_fuse=True           # Enable fused attention\u002FMLP\n)\n\nmodel = AutoModelForCausalLM.from_pretrained(\n    \"TheBloke\u002FMistral-7B-OpenOrca-AWQ\",\n    quantization_config=config\n)\n```\n\n**Note**: Fused modules cannot combine with FlashAttention2.\n\n## vLLM integration\n\n```python\nfrom vllm import LLM, SamplingParams\n\n# vLLM auto-detects AWQ models\nllm = LLM(\n    model=\"TheBloke\u002FLlama-2-7B-AWQ\",\n    quantization=\"awq\",\n    dtype=\"half\"\n)\n\nsampling = SamplingParams(temperature=0.7, max_tokens=200)\noutputs = llm.generate([\"Explain AI\"], sampling)\n```\n\n## Performance benchmarks\n\n### Memory reduction\n\n| Model | FP16 | AWQ 4-bit | Reduction |\n|-------|------|-----------|-----------|\n| Mistral 7B | 14 GB | 5.5 GB | 2.5x |\n| Llama 2-13B | 26 GB | 10 GB | 2.6x |\n| Llama 2-70B | 140 GB | 35 GB | 4x |\n\n### Inference speed (RTX 4090)\n\n| Model | Prefill (tok\u002Fs) | Decode (tok\u002Fs) | Memory |\n|-------|-----------------|----------------|--------|\n| Mistral 7B GEMM | 3,897 | 114 | 5.55 GB |\n| TinyLlama 1B GEMV | 5,179 | 431 | 2.10 GB |\n| Llama 2-13B GEMM | 2,279 | 74 | 10.28 GB |\n\n### Accuracy (perplexity)\n\n| Model | FP16 | AWQ 4-bit | Degradation |\n|-------|------|-----------|-------------|\n| Llama 3 8B | 8.20 | 8.48 | +3.4% |\n| Mistral 7B | 5.25 | 5.42 | +3.2% |\n| Qwen2 72B | 4.85 | 4.95 | +2.1% |\n\n## Custom calibration data\n\n```python\n# Use custom dataset for domain-specific models\nmodel.quantize(\n    tokenizer,\n    quant_config=quant_config,\n    calib_data=\"wikitext\",       # Or custom list of strings\n    max_calib_samples=256,       # More samples = better accuracy\n    max_calib_seq_len=512        # Sequence length\n)\n\n# Or provide your own samples\ncalib_samples = [\n    \"Your domain-specific text here...\",\n    \"More examples from your use case...\",\n]\nmodel.quantize(tokenizer, quant_config=quant_config, calib_data=calib_samples)\n```\n\n## Multi-GPU deployment\n\n```python\nmodel = AutoAWQForCausalLM.from_quantized(\n    \"TheBloke\u002FLlama-2-70B-AWQ\",\n    device_map=\"auto\",  # Auto-split across GPUs\n    max_memory={0: \"40GB\", 1: \"40GB\"}\n)\n```\n\n## Supported models\n\n35+ architectures including:\n- **Llama family**: Llama 2\u002F3, Code Llama, Mistral, Mixtral\n- **Qwen**: Qwen, Qwen2, Qwen2.5-VL\n- **Others**: Falcon, MPT, Phi, Yi, DeepSeek, Gemma\n- **Multimodal**: LLaVA, LLaVA-Next, Qwen2-VL\n\n## Common issues\n\n**CUDA OOM during quantization**:\n```python\n# Reduce batch size\nmodel.quantize(tokenizer, quant_config=quant_config, max_calib_samples=64)\n```\n\n**Slow inference**:\n```python\n# Enable fused layers\nmodel = AutoAWQForCausalLM.from_quantized(model_name, fuse_layers=True)\n```\n\n**AMD GPU support**:\n```python\n# Use ExLlama backend\nconfig = AwqConfig(bits=4, version=\"exllama\")\n```\n\n## Deprecation notice\n\nAutoAWQ is officially deprecated. For new projects, consider:\n- **vLLM llm-compressor**: https:\u002F\u002Fgithub.com\u002Fvllm-project\u002Fllm-compressor\n- **MLX-LM**: For Mac devices with Apple Silicon\n\nExisting quantized models remain usable.\n\n## References\n\n- **Paper**: AWQ: Activation-aware Weight Quantization (arXiv:2306.00978) - MLSys 2024 Best Paper\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fcasper-hansen\u002FAutoAWQ\n- **MIT Han Lab**: https:\u002F\u002Fgithub.com\u002Fmit-han-lab\u002Fllm-awq\n- **Models**: https:\u002F\u002Fhuggingface.co\u002Fmodels?library=awq\n",{"name":5,"tags":44,"author":46,"license":15,"version":47,"category":48,"description":7,"dependencies":49},[45,21,20,22,30,29,36,28,27],"Optimization","Synthetic Sciences","1.0.0","ml-training",[50,51,52],"autoawq","transformers>=4.45.0","torch>=2.0.0","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fawq","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add fadrienne\u002FMyGitHub --skill awq-quantization",{"tool":65,"command":66},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill awq-quantization",{"tool":68,"command":69},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill awq-quantization","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fawq\u002FSKILL.md",true,[73,79],{"path":74,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":77,"sourceHtmlUrl":78},"references\u002Fadvanced-usage.md","REFERENCE","参考文档",7983,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fawq\u002Freferences\u002Fadvanced-usage.md",{"path":80,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Ftroubleshooting.md",7733,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fawq\u002Freferences\u002Ftroubleshooting.md",[84,115,140,174,201,227],{"skillNo":85,"name":86,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":95,"publishedAt":39},"01M7167B28PK20HP7R7ZED2DY0","statistical-power","Sample size calculation, power analysis, and effect size conversion for scientific studies. Use BEFORE data collection (a priori) to determine required N, or after (post-hoc) to compute achieved power. Covers parametric tests (t-test, ANOVA, chi-square, proportions, correlation, regression), simulation-based power for complex\u002Fclustered\u002Fnested designs, and power curves. Use this skill when someone asks 'how many samples do I need', 'what is my power', 'what effect size can I detect', or any variant. Pairs with \u002Fexperimental-design (choose design first, then compute power) and \u002Fstatistical-analysis (analyze data after collection).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-power\u002FSKILL.md",[90],{"categoryNo":91,"categoryName":92,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[96,97,98,99,100,101,102,103,23,104,105,106,107,108,109,110,111,112,113,114],"power","statistical power","effect","scipy","regression","choose","design","uv","Bash","collection","analysis","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"skillNo":116,"name":117,"nameZh":6,"description":118,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":119,"categories":120,"keywords":124,"publishedAt":39},"01M71677EBCM8EPCE9BZCK8505","statistical-analysis","Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-analysis\u002FSKILL.md",[121],{"categoryNo":122,"categoryName":123,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000008","测试与质量",[106,125,96,126,127,128,23,26,129,130,25,131,132,133,134,135,136,137,138,139],"statistical analysis","academic research","implementing","selection","reporting","data","tests","use","with","test","research","results","statsmodels","models","help",{"skillNo":141,"name":142,"nameZh":6,"description":143,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":144,"categories":145,"keywords":155,"publishedAt":39},"01M71673R93XAHFH81DBNVNG7H","scientific-writing","Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA\u002FAMA\u002FVancouver), figures\u002Ftables, reporting guidelines (CONSORT\u002FSTROBE\u002FPRISMA), for research papers and journal submissions.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-writing\u002FSKILL.md",[146,149,150],{"categoryNo":147,"categoryName":148,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":91,"categoryName":92,"taxonomyType":93,"taxonomyTypeDesc":94},{"categoryNo":151,"categoryName":152,"taxonomyType":153,"taxonomyTypeDesc":154},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[156,157,158,159,160,161,162,163,164,165,104,166,167,168,169,170,171,172,129,173],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","read","features",{"skillNo":175,"name":176,"nameZh":6,"description":177,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":178,"categories":179,"keywords":184,"publishedAt":39},"01M7166ZPKXBF4EBPHVTJPQBB1","scientific-visualization","Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib\u002Fseaborn\u002Fplotly with publication styles. For quick exploration use seaborn or plotly directly.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-visualization\u002FSKILL.md",[180,183],{"categoryNo":181,"categoryName":182,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":91,"categoryName":92,"taxonomyType":93,"taxonomyTypeDesc":94},[185,157,186,187,188,189,190,191,192,164,23,193,194,195,196,197,198,199,25,200],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":202,"name":203,"nameZh":6,"description":204,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":205,"categories":206,"keywords":213,"publishedAt":39},"01M7166VXJWQSSC3RQZP60WBGR","research-code","Debug, profile, review, and restructure Python research and analysis code. Use when analysis scripts crash, produce wrong results, run too slowly, or need a correctness review. Covers: reading tracebacks and diagnosing root causes, hypothesis-driven debugging, reviewing code for common statistical\u002Fnumerical errors, profiling data pipelines, improving reproducibility (seeds, environments, determinism), and structuring analysis scripts for sharing. Distinct from \u002Fexploratory-data-analysis (which understands data) and \u002Fstatistical-analysis (which chooses and runs tests) — this skill focuses on the code itself.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fresearch-code\u002FSKILL.md",[207,210],{"categoryNo":208,"categoryName":209,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":211,"categoryName":212,"taxonomyType":153,"taxonomyTypeDesc":154},"01JBSEEDCAT000000000000030","开发工程师",[214,215,135,216,31,217,218,103,219,23,104,106,220,221,222,223,224,225,172,226],"code","research code","which","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":228,"name":229,"nameZh":6,"description":230,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":231,"licenseFileUrl":6,"sourceHtmlUrl":232,"categories":233,"keywords":240,"publishedAt":39},"01M7166CF5HSR7SVZYZFWTDX55","markdown-mermaid-writing","Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fmarkdown-mermaid-writing\u002FSKILL.md",[234,237,238,239],{"categoryNo":235,"categoryName":236,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":147,"categoryName":148,"taxonomyType":93,"taxonomyTypeDesc":94},{"categoryNo":91,"categoryName":92,"taxonomyType":93,"taxonomyTypeDesc":94},{"categoryNo":151,"categoryName":152,"taxonomyType":153,"taxonomyTypeDesc":154},[156,241,242,243,244,245,185,246,247,23,104,106,248,249,250,251,252,253,157,172],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]