# Awq Quantization

> 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.

- **Type:** Skill
- **Install:** `agentstack add skill-orchestra-research-ai-research-skills-awq`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [Orchestra-Research](https://agentstack.voostack.com/s/orchestra-research)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [Orchestra-Research](https://github.com/Orchestra-Research)
- **Source:** https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/main/10-optimization/awq
- **Website:** http://orchestra-research.com

## Install

```sh
agentstack add skill-orchestra-research-ai-research-skills-awq
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# AWQ (Activation-aware Weight Quantization)

4-bit quantization that preserves salient weights based on activation patterns, achieving 3x speedup with minimal accuracy loss.

## When to use AWQ

**Use AWQ when:**
- Need 4-bit quantization with  1
}
```

### GEMV (single-token generation)

```python
quant_config = {
    "version": "GEMV"  # 20% faster for batch_size=1
}
```

**Limitation**: Only batch size 1, not good for large context.

### Marlin (Ampere+ GPUs)

```python
from transformers import AwqConfig, AutoModelForCausalLM

config = AwqConfig(
    bits=4,
    version="marlin"  # 2x faster on A100/H100
)

model = AutoModelForCausalLM.from_pretrained(
    "TheBloke/Mistral-7B-AWQ",
    quantization_config=config
)
```

**Requirements**: Compute Capability 8.0+ (A100, H100, RTX 40xx)

### ExLlamaV2 (AMD compatible)

```python
config = AwqConfig(
    bits=4,
    version="exllama"  # Faster prefill, AMD GPU support
)
```

## HuggingFace Transformers integration

### Direct loading

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "TheBloke/zephyr-7B-alpha-AWQ",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("TheBloke/zephyr-7B-alpha-AWQ")
```

### Fused modules (recommended)

```python
from transformers import AwqConfig, AutoModelForCausalLM

config = AwqConfig(
    bits=4,
    fuse_max_seq_len=512,  # Max sequence length for fusing
    do_fuse=True           # Enable fused attention/MLP
)

model = AutoModelForCausalLM.from_pretrained(
    "TheBloke/Mistral-7B-OpenOrca-AWQ",
    quantization_config=config
)
```

**Note**: Fused modules cannot combine with FlashAttention2.

## vLLM integration

```python
from vllm import LLM, SamplingParams

# vLLM auto-detects AWQ models
llm = LLM(
    model="TheBloke/Llama-2-7B-AWQ",
    quantization="awq",
    dtype="half"
)

sampling = SamplingParams(temperature=0.7, max_tokens=200)
outputs = llm.generate(["Explain AI"], sampling)
```

## Performance benchmarks

### Memory reduction

| Model | FP16 | AWQ 4-bit | Reduction |
|-------|------|-----------|-----------|
| Mistral 7B | 14 GB | 5.5 GB | 2.5x |
| Llama 2-13B | 26 GB | 10 GB | 2.6x |
| Llama 2-70B | 140 GB | 35 GB | 4x |

### Inference speed (RTX 4090)

| Model | Prefill (tok/s) | Decode (tok/s) | Memory |
|-------|-----------------|----------------|--------|
| Mistral 7B GEMM | 3,897 | 114 | 5.55 GB |
| TinyLlama 1B GEMV | 5,179 | 431 | 2.10 GB |
| Llama 2-13B GEMM | 2,279 | 74 | 10.28 GB |

### Accuracy (perplexity)

| Model | FP16 | AWQ 4-bit | Degradation |
|-------|------|-----------|-------------|
| Llama 3 8B | 8.20 | 8.48 | +3.4% |
| Mistral 7B | 5.25 | 5.42 | +3.2% |
| Qwen2 72B | 4.85 | 4.95 | +2.1% |

## Custom calibration data

```python
# Use custom dataset for domain-specific models
model.quantize(
    tokenizer,
    quant_config=quant_config,
    calib_data="wikitext",       # Or custom list of strings
    max_calib_samples=256,       # More samples = better accuracy
    max_calib_seq_len=512        # Sequence length
)

# Or provide your own samples
calib_samples = [
    "Your domain-specific text here...",
    "More examples from your use case...",
]
model.quantize(tokenizer, quant_config=quant_config, calib_data=calib_samples)
```

## Multi-GPU deployment

```python
model = AutoAWQForCausalLM.from_quantized(
    "TheBloke/Llama-2-70B-AWQ",
    device_map="auto",  # Auto-split across GPUs
    max_memory={0: "40GB", 1: "40GB"}
)
```

## Supported models

35+ architectures including:
- **Llama family**: Llama 2/3, Code Llama, Mistral, Mixtral
- **Qwen**: Qwen, Qwen2, Qwen2.5-VL
- **Others**: Falcon, MPT, Phi, Yi, DeepSeek, Gemma
- **Multimodal**: LLaVA, LLaVA-Next, Qwen2-VL

## Common issues

**CUDA OOM during quantization**:
```python
# Reduce batch size
model.quantize(tokenizer, quant_config=quant_config, max_calib_samples=64)
```

**Slow inference**:
```python
# Enable fused layers
model = AutoAWQForCausalLM.from_quantized(model_name, fuse_layers=True)
```

**AMD GPU support**:
```python
# Use ExLlama backend
config = AwqConfig(bits=4, version="exllama")
```

## Deprecation notice

AutoAWQ is officially deprecated. For new projects, consider:
- **vLLM llm-compressor**: https://github.com/vllm-project/llm-compressor
- **MLX-LM**: For Mac devices with Apple Silicon

Existing quantized models remain usable.

## References

- **Paper**: AWQ: Activation-aware Weight Quantization (arXiv:2306.00978) - MLSys 2024 Best Paper
- **GitHub**: https://github.com/casper-hansen/AutoAWQ
- **MIT Han Lab**: https://github.com/mit-han-lab/llm-awq
- **Models**: https://huggingface.co/models?library=awq

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [Orchestra-Research](https://github.com/Orchestra-Research)
- **Source:** [Orchestra-Research/AI-Research-SKILLs](https://github.com/Orchestra-Research/AI-Research-SKILLs)
- **License:** MIT
- **Homepage:** http://orchestra-research.com

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-orchestra-research-ai-research-skills-awq
- Seller: https://agentstack.voostack.com/s/orchestra-research
- Browse the marketplace: https://agentstack.voostack.com/browse

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