Prompt
Specify a webhook-driven task for OpenClaw
Describe event inputs, validation, deduplication, and intended actions before integration work.
OpenClawDevelopment & Engineering
Dave AI
Post-training quantization
Intel Neural Compressor — SOTA low-bit LLM quantization (INT8/FP8/INT4/NVFP4), sparsity, pruning, and distillation for PyTorch, TensorFlow, and ONNX Runtime.
instructions.md
---
name: intel-neural-compressor-b1c650728b1b
description: "Intel Neural Compressor — SOTA low-bit LLM quantization (INT8/FP8/INT4/NVFP4), sparsity, pruning, and distillation for PyTorch, TensorFlow, and ONNX Runtime."
tags: [intel-neural-compressor, quantization, pruning, distillation, inc, intel, zorai]
---
## Overview
Intel Neural Compressor provides low-bit quantization (INT8, FP8, INT4, MXFP4, NVFP4), sparsity, pruning, and knowledge distillation for optimizing models on Intel hardware and beyond.
## Installation
```bash
uv pip install neural-compressor
```
## Basic Quantization
```python
from neural_compressor import Quantization, config
# Post-training quantization
quantizer = Quantization(config)
q_model = quantizer(model)
q_model.save("quantized_model")
```
## Pruning
```python
from neural_compressor import Pruning
pruner = Pruning(model, config={"pruning_type": "snip_momentum", "target_sparsity": 0.3})
pruned_model = pruner.fit()
```
## References
- [Intel NC docs](https://github.com/intel/neural-compressor)
- [Intel NC GitHub](https://github.com/intel/neural-compressor/wiki)---
name: intel-neural-compressor-b1c650728b1b
description: "Intel Neural Compressor — SOTA low-bit LLM quantization (INT8/FP8/INT4/NVFP4), sparsity, pruning, and distillation for PyTorch, TensorFlow, and ONNX Runtime."
tags: [intel-neural-compressor, quantization, pruning, distillation, inc, intel, zorai]
---
## Overview
Intel Neural Compressor provides low-bit quantization (INT8, FP8, INT4, MXFP4, NVFP4), sparsity, pruning, and knowledge distillation for optimizing models on Intel hardware and beyond.
## Installation
```bash
uv pip install neural-compressor
```
## Basic Quantization
```python
from neural_compressor import Quantization, config
# Post-training quantization
quantizer = Quantization(config)
q_model = quantizer(model)
q_model.save("quantized_model")
```
## Pruning
```python
from neural_compressor import Pruning
pruner = Pruning(model, config={"pruning_type": "snip_momentum", "target_sparsity": 0.3})
pruned_model = pruner.fit()
```
## References
- [Intel NC docs](https://github.com/intel/neural-compressor)
- [Intel NC GitHub](https://github.com/intel/neural-compressor/wiki)Platform guidance
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.