Issue Station
Sign In
Tools hereToolbox

vllm - ✅(Solved) Fix [Usage]: 模型返回值reasoning_content [45 pull requests, 3 comments, 3 participants]

Official PRs (…)
ON THIS PAGE

Recommended Tools

×6

Utilities matched from this issue’s tags and category — try them while you read without losing context.

GitHub issue graph ai analysis

Paste a GitHub issue URL. We fetch that issue, discover linked issues from bodies/comments/timeline, collect linked pull requests, and produce a structured English report.

The report is written in English Markdown for sharing and archival.

Helpful · Quick feedback

Loading…
GitHub stats
vllm-project/vllm#38642•Fetched 2026-04-08 01:58:49
View on GitHub
Comments
3
Participants
3
Timeline
4
Reactions
0
Author
hf201429hf201429
Participants
chaunceyjiangchaunceyjianghf201429hf201429KenwwwwKenwwww
Timeline (top)
commented ×3labeled ×1

PR fix notes

PR #39695: Introduce De-dup/Similarity-Check in CI Workflow for PR/Issue

Description (problem / solution / changelog)

Co-Author: Trae + GPT5.3-Codex

Purpose

Example to explain https://github.com/vllm-project/vllm/issues/39694

Example Algorithm:

  • Scoring: 0.75 * text_similarity + 0.25 * file_overlap .
  • Threshold used for report: 0.75 .
  • Using Github Action CI Cache to temp save the Github API result cache for recent 1000 PR/500 issue..etc

Test Plan

Using 1000 recent PR to test the similarity check :

High-similarity pairs ( >=0.75 ): 26

Test Result

PR Similarity

  • Repo: vllm-project/vllm
  • PR count: 1000
  • Candidate pairs: 17375
  • High-similarity pairs (>= 0.75): 26
ScoreTextFilesPR APR B
100%100%100%#39553 Okakarpa shadow clone#39577 Okakarpa shadow clone
99%99%100%#37929 [Core] Use standalone autograd_cache_key for compilation dedup optimization#39517 [Core] Use standalone autograd_cache_key for compilation dedup optimization
96%95%100%#37947 [DRAFT][XPU] Upgrade torch 2.11 for xpu#39257 [XPU] update triton version for torch 2.11 upgrade
96%95%100%#37947 [DRAFT][XPU] Upgrade torch 2.11 for xpu#39313 [XPU] upgrade to triton-xpu 3.7.0
95%97%88%#38249 [Misc] Organize NixlConnector into own directory#39354 [KVConnector][NIXL] Organize NIXL connector into its own directory
95%93%100%#39410 [XPU] Disable fusion passes on XPU Platform#39671 use spawn multiproc method on xpu
94%92%100%#38856 [LMCache] vLLM Block Allocation Event#39719 fix(lmcache): correct store for cached requests while enable prefix cache
94%91%100%#39606 Pass extra_config to the constructor of LMCacheMPXXXAdapter#39719 fix(lmcache): correct store for cached requests while enable prefix cache
94%91%100%#39257 [XPU] update triton version for torch 2.11 upgrade#39313 [XPU] upgrade to triton-xpu 3.7.0
91%100%67%#39432 Gfx1250 wip#39437 Gfx1250 wip rebase test
90%92%85%#36823 [vLLM IR] 3/N fused_add_rms_norm and maybe_inplace#38775 [vLLM IR] 4/N Compile native implementation
90%86%100%#39402 [kv_offload+HMA[10/N]: Support load with multiple KV groups#39403 [kv_offload+HMA][11/N]: Support store with multiple KV groups
86%98%50%#23995 Feature/deepseek v31 lora support#39661 [DOC] Update Gemma 4
82%76%100%#39110 [Core] Disable HMA for eagle/MTP with sliding window models#39376 [Core] Disable HMA for eagle/MTP with sliding window models
82%76%100%#39401 [kv_offload+HMA][9/N]: Support lookup with multiple KV groups#39402 [kv_offload+HMA[10/N]: Support load with multiple KV groups
82%76%100%#39401 [kv_offload+HMA][9/N]: Support lookup with multiple KV groups#39403 [kv_offload+HMA][11/N]: Support store with multiple KV groups
80%96%33%#26583 add log for request trace#39646 V0.12.0 support n sampling delay split to eliminate redundant prefill computation and memory
79%97%22%#35721 [LoRA] Support dual CUDA streams-Linear Layer#37297 [LoRA] Support FP8 LoRA E2E inference-dense model
79%94%32%#39153 [Frontend][4/n] Improve pooling entrypointspooling.
79%74%91%#38775 [vLLM IR] 4/N Compile native implementation#39453 Port activations to IR op 1/3
79%88%50%#39312 [Mergify] Update model vendor auto-label rules#39429 [CI/Build] Update auto-rebase rule
78%100%13%#39723 [SimpleCPUOffloadConnector]: Add support for reset_cache()#39726 [SimpleCPUOffloadConnector]: Add support for reset_cache()
77%98%14%#38780 [vLLM IR][RMSNorm] Port GemmaRMSNorm to vLLM IR Ops#38798 [vLLM IR][RMSNorm] Port RMSNormGated to vLLM IR Ops
77%69%100%#39744 [v1] Expose num_prompt_tokens in CommonAttentionMetadata#39745 [v1] Expose num_prompt_tokens in CommonAttentionMetadata
77%81%62%#23133 Split compressed_tensors_moe.py into separate wna16, int8, fp8, nvfp4#29427 [Refactor] Split up compressed_tensors_moe.py into separate files per method
76%82%59%#39267 [vllm IR] 1/N Port FP8 Quantization to vLLM IR Ops#39481 [vllm IR] Port FP8 Quantization to vLLM IR Ops

Similar Issues:

  • Repo: vllm-project/vllm
  • Issue count: 500
  • Candidate pairs: 9909
  • High-similarity pairs (>= 0.75): 12
Match ScoreDesc SimilarityTitle OverlapIssue AIssue B
100%100%100%#39270 [Bug]: Qwen3.5 crashes when using suffix-decoding#39271 [Bug]: Qwen3.5 crashes when using suffix-decoding
100%100%100%#39372 [Bug]:#39373 [Bug]:
100%100%100%#39372 [Bug]:#39374 [Bug]:
100%100%100%#39373 [Bug]:#39374 [Bug]:
100%100%100%#39433 RFC: Add logit_scale to PoolerConfig for Affine Score Calibration (Platt Scaling)#39434 [RFC]: Add logit_scale to PoolerConfig for Affine Score Calibration (Platt Scaling)
100%100%100%#39299 [Performance] DSV3.2 Indexer: Overlap indexer k+w path
81%95%25%#31888 [Usage]: rollout slow#38642 [Usage]: 模型返回值reasoning_content
80%88%50%#38734 [Transformers v5] SarvamMLAForCausalLM#38740 [Transformers v5] NemotronParseForConditionalGeneration
79%94%20%#29245 [Usage]: 启动 qwen3 vl 超级超级超级慢，sglang 启动很快，可能的原因是什么？#38642 [Usage]: 模型返回值reasoning_content
77%92%17%#29245 [Usage]: 启动 qwen3 vl 超级超级超级慢，sglang 启动很快，可能的原因是什么？#31888 [Usage]: rollout slow
77%89%29%#38384 [Transformers v5] Distributed shutdown test timetout#38740 [Transformers v5] NemotronParseForConditionalGeneration
76%88%31%#31661 [Bug]: jina-reranker-m0 [image_index] IndexError: list index out of range#32151 [Bug]: jina-reranker-m0 infer error
<details> <summary> Essential Elements of an Effective PR Description Checklist </summary>
  • The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
  • The test plan, such as providing test command.
  • The test results, such as pasting the results comparison before and after, or e2e results
  • (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
  • (Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.
</details>

Changed files

  • .github/workflows/detect-duplicate-issues.yml (added, +64/-0)
  • .github/workflows/detect-duplicate-prs.yml (added, +55/-0)
  • .github/workflows/scripts/detect_duplicate_issues.py (added, +453/-0)
  • .github/workflows/scripts/detect_duplicate_prs.py (added, +317/-0)
RAW_BUFFERClick to expand / collapse

Your current environment

模型返回值，这个字段不是reasoning_content吗？新版本修改成了reasoning ？有配置可以设置这个返回key吗？

<img width="1150" height="497" alt="Image" src="https://github.com/user-attachments/assets/28cc011e-8d6f-4590-8267-2c9dcecc6869" />

How would you like to use vllm

I want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.

Before submitting a new issue...

  • Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the documentation page, which can answer lots of frequently asked questions.

extent analysis

TL;DR

Check the documentation or configuration options for vllm to see if there's a setting to change the return key from "reasoning" to "reasoning_content".

Guidance

  • Review the vllm documentation and configuration options to find a setting that controls the return key for model output.
  • Search for issues or FAQs related to customizing the return key in vllm.
  • Check the model's documentation or code to see if it has any built-in options for changing the output key.
  • If no configuration option is found, consider reaching out to the vllm community or support team for further assistance.

Notes

The issue lacks specific technical details about the vllm version, model, or code being used, which makes it difficult to provide a more precise solution.

Recommendation

Apply workaround: Check the vllm documentation and configuration options to find a solution, as upgrading to a fixed version is not mentioned in the issue.

Vote matrix · Quick signals

Works
Did the solution work? Tap to confirm.
Easy Fix
Was it a quick fix?
Time Saver
Did it save you time?
Blocking
Was it severely blocking?
Common Issue
Are others likely hitting this too?
Flaky / Intermittent
Is it intermittent?
Verified / Reproducible
Can you reproduce it reliably?
Loading…
#callback error#memory management#API rate limit#retriever error#indexing error

Still need to ship something?

×6

Another batch ranked right after the header list — different links, same matching logic.

Back to top recommendations

TRENDING

vllm - ✅(Solved) Fix [Usage]: 模型返回值reasoning_content [45 pull requests, 3 comments, 3 participants]