Support gpt-oss #22259

zyongye · 2025-08-05T16:07:08Z

This doc is constantly updated

(Please READ!!) If you want to run this model out-of-box, please follow our recipes or install a custom wheel. This guide is for users who want to build the environment from scratch and do customization on the model. We will slowly merge this PR until we think it is mostly compatible with existing dependencies. Note that current commit branch is only tested against gpt-oss model. Other model may have unexpected behavior.

To download this commit from wheel

uv pip install --pre vllm==0.10.1+gptoss \
    --extra-index-url https://wheels.vllm.ai/gpt-oss/ \
    --extra-index-url https://download.pytorch.org/whl/nightly/cu128 \
    --index-strategy unsafe-best-match

Installation Steps

Create new virtualenv in whatever mechanism:

uv venv
source .venv/bin/activate
uv pip install mcp

Install Pytorch Nightly(Optional for Blackwell user)
- After installation, be sure to uninstall pytorch-triton

uv pip uninstall pytorch-triton

Download Huggingface newest transformer wheel

uv pip install "transformers[torch]"

Download OpenAI triton and install it and triton_kernels (Optional for Blackwell user)

git clone https://github.com/openai/triton.git
pushd triton
uv pip install -r python/requirements.txt
uv pip install -e . --verbose --no-build-isolation
uv pip install -e python/triton_kernels --no-deps
popd

Install new FlashInfer repo (Mandatory for Blackwell user, optional for Hopper user)

uv pip install flashinfer-python==0.2.10

Clone the repo and checkout this PR and build

git clone https://github.com/vllm-project/vllm.git
cd vllm
git checkout 6a70830065701b163e36a86fd331b41b5feac401
python use_existing_torch.py
uv pip install -r requirements/build.txt
uv pip install -U -e . --verbose --no-build-isolation

And let run vllm serve

# On NVIDIA Hopper
vllm serve openai/gpt-oss-120b -tp2 --async-scheduling
# On NVIDIA Blackwell
VLLM_USE_TRTLLM_ATTENTION=1 \
VLLM_USE_TRTLLM_DECODE_ATTENTION=1 \
VLLM_USE_TRTLLM_CONTEXT_ATTENTION=1 \
VLLM_USE_FLASHINFER_MXFP4_MOE=1 \
vllm serve openai/gpt-oss-120b -tp2 --async-scheduling

Known Issue

PyTorch and Triton incompatibility
- This build relies on triton and pytorch features that are not in any stable release yet. So these two packages may cause conflict. More specifically, pytorch nightly may not work with Triton main branch. If that's the case, please revert triton commit until you find working. From our experience, triton commit 663e04e8e3ebed7ee3230a1a7320142689795106 should contain all the feature needed to run this model and yet compatible with any pytorch nightly.
Default memory utilization and batch size will cause CUDA OOM for tp1 on H100. Please increase gpu memory utilization or lower batch size

vllm serve openai/gpt-oss-120b --gpu-memory-utilization 0.95 --max-num-batched-tokens 512

On H100 with tp2, prevent gpu memory utilization from being too high. (0.95 will cause OOM)

Dependency track

Bump transformers (Update transformers to v4.55 #21931)
Bump flashinfer
Bump torch/triton

simon env adjustment Signed-off-by: simon-mo <[email protected]> fix tokenizer and able to startup Signed-off-by: simon-mo <[email protected]> e2e runnable Signed-off-by: simon-mo <[email protected]>

shuffled version working in unit test bug fix have fp8 support on unit test add mxfp4 quant method, implementation is using fp8 until better kernel understanding change config name to be compatible with hf config, model runnable mxfp4 class working, everything still in bf16 preliminary mxfp4 tests Revert "change config name to be compatible with hf config, model runnable" This reverts commit 736bf907fc2f7b028b171b402c19034c0e43c6e8. integrate into model, still bf16 mxfp4 kernel works somehow clean up assertions experimental mxfp4 kernel in vllm cleanup intermediate tensor os the model can run with tp=8 use exact dtype when loading update tests adding swizzle padding in test implement padding to enable hbm_swizzling move quantization to weight loader remove activation padding, only pad the weight remove preallocated tensor in mxfp4 moe method, model can be run with tp=1 move bias post processing after loading to save memory move bias addition to rank 0 formatting verified working

Signed-off-by: simon-mo <[email protected]> weight loading cleanup Signed-off-by: simon-mo <[email protected]> rename oai -> openaimoe for HF compat Signed-off-by: simon-mo <[email protected]> format Signed-off-by: simon-mo <[email protected]> finished rebase Signed-off-by: simon-mo <[email protected]>

Signed-off-by: simon-mo <[email protected]>

… issue

Reduce weight padding since it is handled inside convert_layout function in triton_kernels code refactor works on single GPU inference now

Signed-off-by: simon-mo <[email protected]>

* hf format Signed-off-by: Chen Zhang <[email protected]> * better qkv concat Signed-off-by: Chen Zhang <[email protected]> --------- Signed-off-by: Chen Zhang <[email protected]>

* fix padding for perf Signed-off-by: Hongxia Yang <[email protected]> * simplify and refactor where to do hidden_size padding based on feedback Signed-off-by: Hongxia Yang <[email protected]> * clean up Signed-off-by: Hongxia Yang <[email protected]> --------- Signed-off-by: Hongxia Yang <[email protected]>

Signed-off-by: Yongye Zhu <[email protected]>

huachenheli · 2025-08-06T04:11:01Z

vllm/v1/engine/core.py

        was executed.
        """

+        # Profiler Start and Stop


Can you check if this conflicts with the profile call in EngineCore (passed to model executor)? https://github.com/vllm-project/vllm/blob/main/vllm/v1/engine/core.py#L340

When I tried something similar (#21794), it could cause engine core process to throw so let's make sure we don't break the existing one.

@huachenheli Thanks for reporting it. We are merging the PR step by step, and will not include this part of code. It's only used for our debugging.

ahmeda14960 · 2025-08-06T04:26:16Z

Hi all,

Thanks for the quick work in getting this out! Unfortunately wanted to report I'm running into issues even with the standard uv pip install suggestion:

uv pip install --pre vllm==0.10.1+gptoss \
    --extra-index-url https://wheels.vllm.ai/gpt-oss/ \
    --extra-index-url https://download.pytorch.org/whl/nightly/cu128 \
    --index-strategy unsafe-best-match
  × No solution found when resolving dependencies:
  ╰─▶ Because torchaudio==2.8.0.dev20250804+cu128 depends on torch==2.9.0.dev20250804 and vllm==0.10.1+gptoss depends on
      torch==2.9.0.dev20250804+cu128, we can conclude that torchaudio==2.8.0.dev20250804+cu128 and vllm==0.10.1+gptoss are incompatible.
      And because vllm==0.10.1+gptoss depends on torchaudio==2.8.0.dev20250804+cu128 and you require vllm==0.10.1+gptoss, we can conclude
      that the requirements are unsatisfiable.

And using no cache fails in the same manner... I am trying to install from scratch next:

     $ uv pip install --no-cache --pre vllm==0.10.1+gptoss       --extra-index-url https://wheels.vllm.ai/gpt-oss/       --extra-index-url https://download.pytorch.org/whl/nightly/cu128       --index-strategy unsafe-best-match
  × No solution found when resolving dependencies:
  ╰─▶ Because torchaudio==2.8.0.dev20250804+cu128 depends on torch==2.9.0.dev20250804 and vllm==0.10.1+gptoss depends on torch==2.9.0.dev20250804+cu128, we can conclude that
      torchaudio==2.8.0.dev20250804+cu128 and vllm==0.10.1+gptoss are incompatible.
      And because vllm==0.10.1+gptoss depends on torchaudio==2.8.0.dev20250804+cu128 and you require vllm==0.10.1+gptoss, we can conclude that the requirements are unsatisfiable.
(gptoss) ahmedah@skampere2:~/code/gptoss$

wangxiyuan · 2025-08-06T06:57:57Z

vllm/model_executor/layers/fused_moe/layer.py

                      return_success: bool = False) -> Optional[bool]:
+        # if expert_id is None, then
+        # all the experts are loaded at the same time
+        if not expert_id and self.quant_config.get_name() == "mxfp4":


I tried with BF16 weight https://huggingface.co/unsloth/gpt-oss-20b-BF16/ it raised error when loading weight. Looks the common load logic for bias is missed in FusedMOE.weight_loader

zyongye · 2025-08-06T07:08:25Z

Hi all, thanks for all the comments/reviews on this PR. Given the massive code changes, we decided to split this into small PRs. Please bear with us and report issues after we finish most of the sub-PRs. Thanks for being patient with us.

Also, hardware settings we (vllm team) tested with this PR and are known to work
NVIDIA H100, H200, B200 with TP 1, 2, 4, 8
(EP is currently not supported, at least not through tested)

We haven't tested on any other hardware yet, nor on other models with this PR. So please give us some more time to work on this. Great thanks!!!!

vLLM Teams

XuyangShen · 2025-08-06T08:22:01Z

vllm/model_executor/layers/quantization/mxfp4.py

+                    logical_replica_count=logical_replica_count,
+                )
+
+                return self.fused_experts(


[Bug report]
I encoder the issue when I enabled the EP when serve the oss-120b --enable-expert-parallel:
AttributeError: 'Mxfp4MoEMethod' object has no attribute 'fused_experts'. Did you mean: 'num_experts'?

EP is not currently supported. Running TP is the best method for this PR.

WoosukKwon

We split this PR into smaller PRs and merged considerable portion of them into the main branch.
Now Responses API and MXFP4 integration are pending. We will finish them tmr.

andresC98 · 2025-08-06T10:49:19Z

Any plans on adding Ampere support? (e.g NVIDIA A100 gpus)
#22290

dongZheX · 2025-08-07T05:20:00Z

@zyongye Hello, i try use vllm to infer gpt-oss.
Now i sent a message containing system to control the reasoning effort,

{
    "model": "gpt-oss-120b",
    "messages":[
        {
            "role": "system",
            "content": [{"type": "text", "text": "Reasoning: high"}]
        },
        {
            "role": "user", 
            "content": [
                {"type": "text", "text": "Let $ABCDEF$ be a convex equilateral hexagon in which all pairs of opposite sides are parallel. The triangle whose sides are extensions of segments $\\overline{AB}$ , $\\overline{CD}$ , and $\\overline{EF}$ has side lengths $200, 240,$ and $300$ . Find the side length of the hexagon.\n\nPlease reason step by step, and put your final answer within \\boxed{}."}
            ]
        }
    ],
    "temperature": 0.0001,
    "max_tokens": 1,
    "top_p": 0.001,
    "logprobs": true,
    "echo": true
}

But the final input give to the model is:

system<|message|>You are ChatGPT, a large language model trained by OpenAI.
Knowledge cutoff: 2024-06
Current date: 2025-08-07

Reasoning: medium

# Valid channels: analysis, commentary, final. Channel must be included for every message.<|end|><|start|>developer<|message|><|end|><|start|>system<|message|>Reasoning: high<|end|><|start|>user<|message|>Let $ABCDEF$ be a convex equilateral hexagon in which all pairs of opposite sides are parallel. The triangle whose sides are extensions of segments $\overline{AB}$ , $\overline{CD}$ , and $\overline{EF}$ has side lengths $200, 240,$ and $300$ . Find the side length of the hexagon.

Please reason step by step, and put your final answer within \boxed{}.<|end|><|start|>assistant

So even though I specified "Reasoning: high" in my input, the model still seems to behave as if "Reasoning: medium" is active. And the aime25(avg@32) is only 76.67.

Just want to confirm whether this is intended.

fanjikang · 2025-08-07T08:25:45Z

@dsingal0 I encountered the same error. This happens because the openai_harmony library tries to automatically download a special encoding file (e.g., o200k_base.tiktoken) from the internet, but if it cannot (due to firewalls, no internet, or missing file), it fails.
1.Manually Download the Tokenizer
wget https://openaipublic.blob.core.windows.net/encodings/o200k_base.tiktoken mv o200k_base.tiktoken fb374d419588a4632f3f557e76b4b70aebbca790 (The new filename is the SHA1 hash that tiktoken/openai_harmony expects for this encoding.)
2.Set the Cache Directory for tiktoken/openai_harmony
export TIKTOKEN_RS_CACHE_DIR=/your/path/

- Add GPT-OSS model implementation (GptOssForCausalLM) - Add MXFP4 quantization support for efficient inference - Add Harmony utilities for reasoning capabilities - Add MCP tool server integration with demo tools - Add CLI argument for tool server configuration - Add example script for serving GPT-OSS with vLLM - Update model registry to include GPT-OSS - Add openai-harmony dependency for GPT-OSS features Key components: * GPT-OSS model with SwiGLU activation and RMSNorm * MXFP4 quantization method for 4-bit weights * Tool server with MCP protocol support * Harmony encoding for reasoning tokens * Example usage script with reasoning and tools This is the first part of implementing GPT-OSS support from vllm-project#22259

Major additions: - Extended OpenAI protocol with reasoning support - Added include_reasoning parameter to ChatCompletionRequest - Enhanced UsageInfo with reasoning_tokens tracking - Added reasoning field to ChatCompletionResponse - Model Context Protocol (MCP) implementation - Comprehensive test suite for GPT-OSS functionality - Production-ready example with configuration guide Features: - Full reasoning content parsing and streaming - Tool integration with MCP protocol - Token usage tracking for reasoning vs final content - Backward compatible API extensions - Complete end-to-end GPT-OSS workflow example This completes the core GPT-OSS implementation from PR vllm-project#22259

…ocumentation - Fixed missing imports (torch, time, json, re) in protocol.py - Added comprehensive implementation status documentation - Updated test and example files with latest enhancements - Ready for production deployment This completes the full GPT-OSS integration from vLLM PR vllm-project#22259

bnellnm · 2025-08-07T17:59:44Z

vllm/model_executor/layers/fused_moe/fused_moe.py

+        w1_bias: Optional[torch.Tensor],
+        w2_bias: Optional[torch.Tensor],


How are these biases different from the zero points? Or, why not just use the existing _zp arguments?

Good point. They are added to the end of matmul per expert. I am not sure _zp has the exact functionality, and that could be across other experts. But either way, I have no plan to add these lines in main

Afaik, the current _zp args are only used by the int4/int8 triton moe implementation in fused_moe.py.

zyongye · 2025-08-07T18:13:17Z

Any plans on adding Ampere support? (e.g NVIDIA A100 gpus) #22290

@andresC98 Yes. Our current plan is to gradually roll down hardware support (blackwell -> hopper -> ampere). We have all the kernel integrated already to run this on Ampere. We just haven't tested end-to-end yet.

LucasWilkinson · 2025-08-07T20:42:05Z

Any plans on adding Ampere support? (e.g NVIDIA A100 gpus) #22290

Wheel updated: #22290 (comment)

rohitharkhani · 2025-08-08T04:11:42Z

using the vllm/vllm-openai:gptoss image gives the following error
(APIServer pid=16)   File "/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/openai/serving_responses.py", line 130, in __init__ 

(APIServer pid=16)     get_stop_tokens_for_assistant_actions()) 

(APIServer pid=16)     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 

(APIServer pid=16)   File "/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/harmony_utils.py", line 187, in get_stop_tokens_for_assistant_actions 

(APIServer pid=16)     return get_encoding().stop_tokens_for_assistant_actions() 

(APIServer pid=16)            ^^^^^^^^^^^^^^ 

(APIServer pid=16)   File "/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/harmony_utils.py", line 37, in get_encoding 

(APIServer pid=16)     _harmony_encoding = load_harmony_encoding( 

(APIServer pid=16)                         ^^^^^^^^^^^^^^^^^^^^^^ 

(APIServer pid=16)   File "/usr/local/lib/python3.12/dist-packages/openai_harmony/__init__.py", line 674, in load_harmony_encoding 

(APIServer pid=16)     inner: _PyHarmonyEncoding = _load_harmony_encoding(name) 

(APIServer pid=16)                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 

(APIServer pid=16) openai_harmony.HarmonyError: error downloading or loading vocab file: failed to download or load vocab file 
This is because the openai-harmony Python module used in vllm attempts to download the tiktoken file from the Internet.

Tracking OpenAI's Harmony, it ultimately executes the following Rust code. https://github.com/openai/harmony/blob/9528c7b4a00a3307fd9685fc1328aee11c3d9c90/src/tiktoken_ext/public_encodings.rs#L417

Looking at the code above, you can see that if the file is cached, it does not download it but reads it from the cache. As a temporary solution, I downloaded the tiktoken file in a Linux environment with internet access and copied the cached directory to resolve the issue.

This is what helped by building my own cache after reading harmony code.

Basically i downloaded and set end variable during docker build to avoid runtime download.

RUN mkdir /vllm-workspace/tiktoken
RUN wget -O /vllm-workspace/tiktoken/o200k_base.tiktoken https://openaipublic.blob.core.windows.net/encodings/o200k_base.tiktoken
RUN wget -O /vllm-workspace/tiktoken/cl100k_base.tiktoken https://openaipublic.blob.core.windows.net/encodings/cl100k_base.tiktoken
ENV TIKTOKEN_ENCODINGS_BASE /vllm-workspace/tiktoken/

foreverlms · 2025-08-11T13:07:55Z

vllm/model_executor/layers/quantization/mxfp4.py

+        w2_weight = torch.nn.Parameter(torch.zeros(
+            num_experts,
+            hidden_size,
+            intermediate_size_per_partition_after_pad // 2,


QES:

Why will w13_weight, w13_weight_scale, w2_weight be padded to create a bigger tensor than dim after normally tp?

Why won't w2_bias be padded too?

Thanks for clarification.

These are for creating weight tensors. Paddings are added for 1) kernel requirement, 2) boost performance. And inside mxfp4, only intermediate size pads are needed and w2 doesn't have that parameter. Hidden state pad are calculated in different place like here.

vllm/vllm/model_executor/layers/fused_moe/layer.py

Lines 750 to 753 in 4678503

if (current_platform.is_rocm()

or envs.VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8

or envs.VLLM_USE_FLASHINFER_MOE_MXFP4_BF16):

hidden_size = round_up(hidden_size, 256)

foreverlms · 2025-08-11T13:08:38Z

vllm/model_executor/layers/quantization/mxfp4.py

+            return
+        from triton_kernels.matmul_ogs import FlexCtx, PrecisionConfig
+
+        w13_bias = layer.w13_bias.to(torch.float32)


For triton kernel path, why we promote bias to float32?

This is specified by the triton kernel, upcast is only to comply with its requirement.

foreverlms · 2025-08-11T13:11:46Z

vllm/model_executor/models/gpt_oss.py

+                    weight = weight[ep_rank_start:ep_rank_end, ...]
+                else:
+                    # (only load on rank 0 to avoid duplication)
+                    if tp_rank != 0:


For tp_rank = 0, we load the full w2_bias. For tp_rank != 0, we just set it to zero. Won't this cause problems of computing? Or we will broadcast to other ranks after rank=0 loading? Actually from the runtime, I found these values for rank!=0 are indeed zeros. Thanks for clarification.

The MoE layer will perform allreduce at the very end. So if we load w2_bias for every rank. It will be added multiple times, which is not correct.

longern · 2025-08-12T02:32:43Z

vllm/model_executor/models/gpt_oss.py

+    ):
+        super().__init__()
+        self.vllm_config = vllm_config
+        self.model_config = vllm_config.model_config.hf_config


Here model_config is used instead of config in other models. Would this affect LoRA compatibility?
For example, in qwen3:

vllm/vllm/model_executor/models/qwen3.py

Line 283 in 1891a26

self.config = config

We haven't explored adding LoRA yet.

fernandaspets · 2025-08-14T09:35:55Z

Hi does this work with rtx pro 6000 blackwells? or 50 series blackwells. I think they are both sm120

zyongye · 2025-08-25T00:09:04Z

Close this issue since we have merged all of it for release 0.10.1

LiuXiaoxuanPKU and others added 30 commits July 25, 2025 16:27

working integration with flash attention with sinks

8024a5f

simon env adjustment Signed-off-by: simon-mo <[email protected]> fix tokenizer and able to startup Signed-off-by: simon-mo <[email protected]> e2e runnable Signed-off-by: simon-mo <[email protected]>

ran use_existing_torch.py

60f6b5a

Signed-off-by: simon-mo <[email protected]>

new weight format works for tp1 and non-swizzling

6cb5e41

unpadded tp2 works

53165c9

new format swizzling

7b380a1

add non-uniform tp sharding for moe layer

5b012bf

fotmating

e6ca55e

comply with upstream triton_kernel refactor

ab2f28e

working on blackwell

4ad40a7

adding constraints to use persistent kernel

2a05b31

add epilogue_subtile constraint

88b2e85

update on latest triton change, use non-swizzling temporially for nan…

3d1da66

… issue

rms default value

4e2a184

swiglu limit

9653279

triton kernel fixed, enable swizzling

18f2b76

Remove activation padding

7707b9d

Reduce weight padding since it is handled inside convert_layout function in triton_kernels code refactor works on single GPU inference now

new model path and make it configurable

c85eb70

Signed-off-by: simon-mo <[email protected]>

fix lint

1eb375c

Signed-off-by: simon-mo <[email protected]>

Support huggingface format (vllm-project#10)

81ffa34

* hf format Signed-off-by: Chen Zhang <[email protected]> * better qkv concat Signed-off-by: Chen Zhang <[email protected]> --------- Signed-off-by: Chen Zhang <[email protected]>

Basic Harmony Integration (vllm-project#9)

c652a92

Enable open weight model on ROCm

b499376

batched ep working with pplx kernels

75be1af

change num_experts name in config

0c190f7

deep_ep working, need padding hacks

cd077b5

Supports built-in web search & python (vllm-project#12)

b922d33

move swizzle_mxfp4 to mxfp4_util.py

e8d69e4

add swiglu_alpha and limits as parameter

edc111e

one more registry

6a70830

Signed-off-by: Yongye Zhu <[email protected]>

huachenheli reviewed Aug 6, 2025

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zyongye mentioned this pull request Aug 6, 2025

Update transformers to v4.55 #21931

Merged

wangxiyuan reviewed Aug 6, 2025

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XuyangShen reviewed Aug 6, 2025

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WoosukKwon reviewed Aug 6, 2025

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echo-cool mentioned this pull request Aug 6, 2025

[Bug]: gpt-oss -> FA3 not detected on RTX 5090 (Blackwell) – Sinks are only supported in FlashAttention 3 #22279

Closed

1 task

jthakurH mentioned this pull request Aug 7, 2025

Add GPT-OSS Model Support with MXFP4 Quantization and Flash Attention 3 HabanaAI/vllm-fork#1723

Closed

weedge mentioned this pull request Aug 7, 2025

Feat: add vllm + openai_gpt_oss on modal(use queue to local input chat) to run ai-bot-pro/achatbot#180

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bnellnm reviewed Aug 7, 2025

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mergify bot added the gpt-oss Related to GPT-OSS models label Aug 11, 2025

foreverlms reviewed Aug 11, 2025

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Staberinde mentioned this pull request Aug 11, 2025

Unable to run gpt_oss model type runpod-workers/worker-vllm#206

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longern reviewed Aug 12, 2025

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zyongye closed this Aug 25, 2025

		w1_bias: Optional[torch.Tensor],
		w2_bias: Optional[torch.Tensor],

	if (current_platform.is_rocm()
	or envs.VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8
	or envs.VLLM_USE_FLASHINFER_MOE_MXFP4_BF16):
	hidden_size = round_up(hidden_size, 256)

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This doc is constantly updated

To download this commit from wheel

Installation Steps

Known Issue

Dependency track

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fernandaspets commented Aug 14, 2025

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zyongye commented Aug 25, 2025

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zyongye commented Aug 5, 2025 •

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