[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6M9RGCGTYSGF1A5PPP93V70":3,"skill-same:01M6M9RGCGTYSGF1A5PPP93V70":86},{"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":41,"linkOnly":42,"markdownContent":43,"markdownBody":44,"frontMatter":45,"refName":52,"commitSha":53,"skillDirectory":54,"forksCount":55,"repositoryHtmlUrl":56,"repositoryHomepageUrl":57,"ownerHtmlUrl":58,"ownerBlogUrl":59,"ownerDescription":60,"updatedAt":61,"install":62,"resources":75},"01M6M9RGCGTYSGF1A5PPP93V70","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.","hashgraph-online\u002Fawesome-codex-plugins","hashgraph-online","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160371962?v=4","01M6CNB6NRJVNNGN14TR01Q6D3","HOL (Hashgraph Online)","01M6JT1HSKKG9V68KQAQGBA7XP",1571,"MIT","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fawq-quantization\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40],"awq quantization","Quantization","AWQ","Activation-Aware","when","2024","accuracy","for","you","GPTQ","Marlin Kernels","vLLM Integration","Memory Optimization","memory","4-Bit","bit","activation","Transformers","Fast Inference","Optimization","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.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  category: ml-training\n  maintainer: Kalaris Labs\n  tags: Optimization, AWQ, Quantization, 4-Bit, Activation-Aware, Memory Optimization, Fast Inference, vLLM Integration, Marlin Kernels\n  dependencies: 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\n## Agent operating procedure\n\n1. **Check the environment.** Check GPU type, memory and driver\u002FCUDA versions (`nvidia-smi`), framework versions, and dataset location and size.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Do a smoke run: tiny model or subset, few steps, and confirm loss decreases and checkpoints save.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Track metrics on held-out data, compare against a baseline, and record seeds, configs and hardware.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| CUDA out-of-memory | Reduce batch size, enable gradient accumulation\u002Fcheckpointing or mixed precision, or shard the model. |\n| Loss is NaN or diverges | Lower the learning rate, check data for invalid values, and enable gradient clipping. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Never claim training results without logs; estimate compute cost before launching large jobs.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n\n## Related skills\n\n- `quantizing-models-bitsandbytes`: Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss.\n- `gptq`: Quantizes LLMs to 4-bit (also 3-bit) with GPTQ using group-wise quantization (group size 128 by default), via AutoGPTQ and transformers.\n- `serving-llms-vllm`: Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching.\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\n## Agent operating procedure\n\n1. **Check the environment.** Check GPU type, memory and driver\u002FCUDA versions (`nvidia-smi`), framework versions, and dataset location and size.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Do a smoke run: tiny model or subset, few steps, and confirm loss decreases and checkpoints save.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Track metrics on held-out data, compare against a baseline, and record seeds, configs and hardware.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| CUDA out-of-memory | Reduce batch size, enable gradient accumulation\u002Fcheckpointing or mixed precision, or shard the model. |\n| Loss is NaN or diverges | Lower the learning rate, check data for invalid values, and enable gradient clipping. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Never claim training results without logs; estimate compute cost before launching large jobs.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n\n## Related skills\n\n- `quantizing-models-bitsandbytes`: Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss.\n- `gptq`: Quantizes LLMs to 4-bit (also 3-bit) with GPTQ using group-wise quantization (group size 128 by default), via AutoGPTQ and transformers.\n- `serving-llms-vllm`: Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching.\n",{"name":5,"license":15,"metadata":46,"description":7},{"tags":47,"version":48,"category":49,"maintainer":50,"dependencies":51},"Optimization, AWQ, Quantization, 4-Bit, Activation-Aware, Memory Optimization, Fast Inference, vLLM Integration, Marlin Kernels","1.0.0","ml-training","Kalaris Labs","autoawq, transformers>=4.45.0, torch>=2.0.0","main","2b63f856ce19c2a797929cacabd9e0fc936812fa","plugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fawq-quantization",490,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins","https:\u002F\u002Fhol.org\u002Fplugins\u002Fbest-codex-plugins","https:\u002F\u002Fgithub.com\u002Fhashgraph-online","https:\u002F\u002Fhol.org","Open-source standards and developer tools for interoperable AI agents: discovery, identity, communication, payments, privacy and security.","2026-11-14 02:12:02",{"source":8,"skillName":5,"commands":63,"prompt":73,"zipAvailable":74},[64,67,70],{"tool":65,"command":66},"npx","npx skills add hashgraph-online\u002Fawesome-codex-plugins --skill awq-quantization",{"tool":68,"command":69},"bunx","bunx skills add hashgraph-online\u002Fawesome-codex-plugins --skill awq-quantization",{"tool":71,"command":72},"pnpm","pnpm dlx skills add hashgraph-online\u002Fawesome-codex-plugins --skill awq-quantization","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fawq-quantization\u002FSKILL.md",true,[76,82],{"path":77,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":80,"sourceHtmlUrl":81},"references\u002Fadvanced-usage.md","REFERENCE","参考文档",7983,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fawq-quantization\u002Freferences\u002Fadvanced-usage.md",{"path":83,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":84,"sourceHtmlUrl":85},"references\u002Ftroubleshooting.md",7733,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fawq-quantization\u002Freferences\u002Ftroubleshooting.md",[87,96,103,110,134,159],{"skillNo":88,"name":89,"nameZh":6,"description":90,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":91,"licenseFileUrl":6,"sourceHtmlUrl":92,"categories":93,"keywords":94,"publishedAt":95},"01M746ZWACVKMZ1BJXJZHYJSSX","migrate-to-e2e","Moves an existing Playwright, Cypress, Selenium, Appium, Detox, Maestro, or TesterArmy suite to e2e one slice at a time, with the old suite running beside the new one until each ported test is proven. Keeps a MIGRATION.md progress file so any later session resumes where the last stopped. Optionally plants a defect to show both suites catch it. Use for \u002Fmigrate-to-e2e, \"migrate our tests to e2e\", \"port these Maestro flows\", \"move our TesterArmy tests\", or when a project has an e2e config and an older suite still running.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fmigrate-to-e2e\u002FSKILL.md",[],[],"2026-11-11 15:11:14",{"skillNo":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":91,"licenseFileUrl":6,"sourceHtmlUrl":100,"categories":101,"keywords":102,"publishedAt":95},"01M746ZPTAX1Q0FBKAB1CYND7E","e2e","Writes, runs, and debugs end-to-end tests for web and mobile apps with e2e, the agentic test runner, which mixes agent.act goals with exact locator assertions on a Playwright browser or an iOS or Android device. Also drives a project's running app live over the e2e MCP server to look at a screen, check a UI change, take a screenshot or record a video, and try a locator; and runs bug bashes, parallel e2e explore runs with each bug proven by a repro test. Use when a project depends on e2e or has an e2e.config.ts, when asked for end-to-end, browser, mobile, or agentic UI tests, to check a change in the running app, to QA or bug bash an app or branch, or when an e2e run fails.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fe2e\u002FSKILL.md",[],[],{"skillNo":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":91,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":109,"publishedAt":95},"01M746ZJN784HVHMDP9MQGCSGG","create-verification-skill","Generate a project-local verify-\u003Capp> skill on top of e2e, so any coding agent can launch the app, check it is worth driving, exercise a feature the way a user does, keep evidence, and bug bash it. Writes the skill, a feature map with one explore charter per feature, and proves it once. Use for \u002Fcreate-verification-skill, \"make a verification skill for this repo\", or when a project has no scripted way to prove UI behavior.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fcreate-verification-skill\u002FSKILL.md",[],[],{"skillNo":111,"name":112,"nameZh":6,"description":113,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":91,"licenseFileUrl":6,"sourceHtmlUrl":114,"categories":115,"keywords":116,"publishedAt":41},"01M716A6ETNB9B1SQ3GXZC75VR","deja-search","deja-vu memory — search the user's past AI coding sessions with the deja CLI. Holds what they already settled and never wrote into the repo (naming rules, banned or already-tried tools, required checks before merge, preferred values, who owns what). Use before your first edit in a task, before you call a change done or ready to merge, before you recommend a tool, library, design or reviewer, before re-debugging an error that may already be solved, when they say 'didn't we fix this before', 'what did we decide about X' or 'I already have that', and before telling them something on this machine does not exist.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-search\u002FSKILL.md",[],[117,118,119,120,121,32,122,123,124,125,126,23,27,127,128,129,130,131,132,133],"search","deja search","never","error","ready","re","decide","fix","design","debugging","done","reviewer","io","change","cli","merge","past",{"skillNo":135,"name":136,"nameZh":6,"description":137,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":91,"licenseFileUrl":6,"sourceHtmlUrl":138,"categories":139,"keywords":140,"publishedAt":41},"01M716A3J2W5SENSE9VGMXB375","deja-reflect","End-of-session reflection and memory curation with deja-vu. Review what was decided, what commands broke and what repaired them, and record durable project rules or decisions with deja remember so the next session starts with them.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-reflect\u002FSKILL.md",[],[141,142,143,129,144,145,146,147,148,149,150,151,152,153,32,154,155,156,157,158],"deja reflect","reflect","review","with","record","github","next","https","node","Rules","decisions","brew","remember","project rules","project","session","end","reflection",{"skillNo":160,"name":161,"nameZh":6,"description":162,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":91,"licenseFileUrl":6,"sourceHtmlUrl":163,"categories":164,"keywords":176,"publishedAt":41},"01M715T3RR4BAYSR57HPPTJ0PF","revid","Make, edit, export and post AI videos with Revid (revid.ai) through the Revid MCP server. Use whenever the user wants a short-form or long-form video made (from an idea, a script, a URL, an article, a PDF, a tweet, a product page, their own clips, or a song), wants to find what is going viral on TikTok, YouTube, Instagram or X before making something, wants to edit or re-export an existing Revid project, generate a standalone AI image or clip, make a series episode, or publish and schedule a finished video to connected social accounts. Covers setup (hosted MCP with OAuth, or an API key), the 36 tools, credits, polling and the rules for publishing. Routes to the more specific revid-* skills when one fits.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftmakersas\u002Frevid-skills\u002Fskills\u002Frevid\u002FSKILL.md",[165,170,173],{"categoryNo":166,"categoryName":167,"taxonomyType":168,"taxonomyTypeDesc":169},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",{"categoryNo":171,"categoryName":172,"taxonomyType":168,"taxonomyTypeDesc":169},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":174,"categoryName":175,"taxonomyType":168,"taxonomyTypeDesc":169},"01JBSEEDCAT000000000000010","后端与接口",[161,177,178,179,180,181,182,155,183,122,184,185,186,187,23,188,189,190,191,192],"URL","Video","short-form","standalone","PDF","viral","tiktok","short","social","MCP","song","image","mcp server","schedule","api","publish"]