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Merged
merged 3 commits into from
Oct 21, 2025
Merged

Fixing broken internal links #1955

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7de0dca
Fixing broken internal links
aireilly Oct 21, 2025
b866120
Merge branch 'main' into fix-links
dsikka Oct 21, 2025
d02c3c3
Merge branch 'main' into fix-links
shanjiaz Oct 21, 2025
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2 changes: 1 addition & 1 deletion .gitignore
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Expand Up @@ -126,7 +126,7 @@ venv.bak/

# mkdocs documentation
/site
docs/.cache/
docs/.cache/*

# mypy
.mypy_cache/
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10 changes: 5 additions & 5 deletions docs/index.md
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Expand Up @@ -20,7 +20,7 @@ Review the [LLM Compressor v0.8.0 release notes](https://github.com/vllm-project
!!! info "Support for multiple modifiers in oneshot compression runs"
LLM Compressor now supports using multiple modifiers in oneshot compression runs such as applying both AWQ and GPTQ in a single model.

Using multiple modifiers is an advanced usage of LLM Compressor and an active area of research. See [Non-uniform Quantization](examples/quantization_non_uniform/) for more detail and example usage.
Using multiple modifiers is an advanced usage of LLM Compressor and an active area of research. See [Non-uniform Quantization](/examples/quantization_non_uniform/) for more detail and example usage.

!!! info "Quantization and calibration support for Qwen3 models"
Quantization and calibration support for Qwen3 Next models has been added to LLM Compressor.
Expand All @@ -39,16 +39,16 @@ Review the [LLM Compressor v0.8.0 release notes](https://github.com/vllm-project
## Recent Updates

!!! info "QuIP and SpinQuant-style Transforms"
The newly added [`QuIPModifier`](../examples/transform/quip_example.py) and [`SpinQuantModifier`](../examples/transform/spinquant_example.py) allow you to quantize models after injecting hadamard weights into the computation graph, reducing quantization error and greatly improving accuracy recovery for low bit-weight and activation quantization.
The newly added [`QuIPModifier` and `SpinQuantModifier`](/examples/transform) transforms allow you to quantize models after injecting hadamard weights into the computation graph, reducing quantization error and greatly improving accuracy recovery for low bit-weight and activation quantization.

!!! info "DeepSeekV3-style Block Quantization Support"
Allows for more efficient compression of large language models without needing a calibration dataset. Quantize a Qwen3 model to [W8A8](../examples/quantization_w8a8_fp8/fp8_block_example.py).
Allows for more efficient compression of large language models without needing a calibration dataset. Quantize a Qwen3 model to [W8A8](/examples/quantization_w8a8_fp8/).

!!! info "FP4 Quantization - now with MoE and non-uniform support"
Quantize weights and activations to FP4 and seamlessly run the compressed model in vLLM. Model weights and activations are quantized following the [NVFP4 configuration](https://github.com/neuralmagic/compressed-tensors/blob/f5dbfc336b9c9c361b9fe7ae085d5cb0673e56eb/src/compressed_tensors/quantization/quant_scheme.py#L104). See examples of [FP4 activation support](../examples/quantization_w4a4_fp4/llama3_example.py), [MoE support](../examples/quantization_w4a4_fp4/qwen_30b_a3b.py), and [Non-uniform quantization support](../examples/quantization_non_uniform/README.md) where some layers are selectively quantized to FP8 for better recovery. You can also mix other quantization schemes, such as INT8 and INT4.
Quantize weights and activations to FP4 and seamlessly run the compressed model in vLLM. Model weights and activations are quantized following the [NVFP4 configuration](https://github.com/neuralmagic/compressed-tensors/blob/f5dbfc336b9c9c361b9fe7ae085d5cb0673e56eb/src/compressed_tensors/quantization/quant_scheme.py#L104). See examples of [FP4 activation support](/examples/quantization_w4a4_fp4/), [MoE support](/examples/quantization_w4a4_fp4/), and [Non-uniform quantization support](/examples/quantization_non_uniform/) where some layers are selectively quantized to FP8 for better recovery. You can also mix other quantization schemes, such as INT8 and INT4.

!!! info "Llama4 Quantization Support"
Quantize a Llama4 model to [W4A16](../examples/quantization_w4a16) or [NVFP4](../examples/quantization_w4a4_fp4/llama4_example.py). The checkpoint produced can seamlessly run in vLLM.
Quantize a Llama4 model to [W4A16](/examples/quantization_w4a16/) or [NVFP4](/examples/quantization_w4a4_fp4/). The checkpoint produced can seamlessly run in vLLM.

For more information, check out the [latest release on GitHub](https://github.com/vllm-project/llm-compressor/releases/latest).

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