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[None][feat] Default GPT-OSS to the Python KV-cache transceiver - #16479

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nv-xtf merged 2 commits into
NVIDIA:mainfrom
nv-xtf:dev-tingfengx-gpt-oss-transceiver-v2-default
Jul 20, 2026
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[None][feat] Default GPT-OSS to the Python KV-cache transceiver#16479
nv-xtf merged 2 commits into
NVIDIA:mainfrom
nv-xtf:dev-tingfengx-gpt-oss-transceiver-v2-default

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@nv-xtf nv-xtf commented Jul 16, 2026

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Description

Built on top of #16164 (per-model transceiver runtime auto selection): opt GPT-OSS into the Python KV-cache transceiver by default.

GptOssForCausalLM now overrides get_preferred_transceiver_runtime() to return "PYTHON". Disaggregated deployments that leave transceiver_runtime at its default auto (backend NIXL/DEFAULT) use the Python transceiver (KvCacheTransceiverV2) out of the box. Explicit user settings always take precedence, and incompatible backends fall back to the C++ transceiver per the #16164 resolution semantics. No effect on non-disaggregated deployments.

Test Coverage

  • New unit test: test_modeling_gpt_oss.py::test_gpt_oss_prefers_python_transceiver pins the model preference.
  • The resolution chain (auto → model hook → backend compatibility) is covered by the [None][feat] BREAKING: add per-model transceiver runtime auto selection #16164 unit tests (TestTransceiverRuntimeAutoResolution).
  • Verified end-to-end with test_disaggregated_gpt_oss_120b_harmony: startup logs show Resolved transceiver_runtime='auto' to 'PYTHON' for GptOssForCausalLM and Using KvCacheTransceiverV2; explicit CPP override verified to bypass the preference.

PR Checklist

Please review the following before submitting your PR:

  • PR description clearly explains what and why. If using CodeRabbit's summary, please make sure it makes sense.

  • PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.

  • Test cases are provided for new code paths (see test instructions)

  • If PR introduces API changes, an appropriate PR label is added - either api-compatible or api-breaking. For api-breaking, include BREAKING in the PR title.

  • Any new dependencies have been scanned for license and vulnerabilities

  • CODEOWNERS updated if ownership changes

  • Documentation updated as needed

  • Update tava architecture diagram if there is a significant design change in PR.

  • The reviewers assigned automatically/manually are appropriate for the PR.

  • Please check this after reviewing the above items as appropriate for this PR.

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Summary by CodeRabbit

  • Bug Fixes

    • Improved GPT-OSS runtime compatibility by selecting the Python transceiver runtime.
  • Tests

    • Added coverage to verify the preferred transceiver runtime is selected correctly.

@nv-xtf
nv-xtf requested a review from a team as a code owner July 16, 2026 06:42
@nv-xtf
nv-xtf requested a review from yechank-nvidia July 16, 2026 06:42
@nv-xtf
nv-xtf requested a review from Shixiaowei02 July 16, 2026 06:43
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Review Change Stack

📝 Walkthrough

Walkthrough

GPT-OSS now declares "PYTHON" as its preferred transceiver runtime, with explicit typing annotations and a unit test asserting the returned value.

Changes

GPT-OSS transceiver runtime

Layer / File(s) Summary
Declare and validate Python runtime preference
tensorrt_llm/_torch/models/modeling_gpt_oss.py, tests/unittest/_torch/modeling/test_modeling_gpt_oss.py
GptOssForCausalLM adds a typed class method returning "PYTHON", and a unit test verifies the preference.

Estimated code review effort: 1 (Trivial) | ~5 minutes

Suggested reviewers: yechank-nvidia

🚥 Pre-merge checks | ✅ 5
✅ Passed checks (5 passed)
Check name Status Explanation
Docstring Coverage ✅ Passed No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.
Linked Issues check ✅ Passed Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check ✅ Passed Check skipped because no linked issues were found for this pull request.
Title check ✅ Passed The title follows the repository template and clearly summarizes the main change.
Description check ✅ Passed The description includes Description, Test Coverage, and PR Checklist sections and is sufficiently complete.
✨ Finishing Touches
🧪 Generate unit tests (beta)
  • Create PR with unit tests

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nv-xtf commented Jul 16, 2026

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/bot run --disable-fail-fast

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PR_Github #59647 [ run ] triggered by Bot. Commit: a5219f6 Link to invocation

Shixiaowei02 added a commit to Shixiaowei02/TensorRT-LLM that referenced this pull request Jul 16, 2026
Mirror the GPT-OSS approach (NVIDIA#16479): KimiK25ForConditionalGeneration now
declares "PYTHON" as its preferred KV-cache transceiver runtime. In
disaggregated serving, when the user leaves
cache_transceiver_config.transceiver_runtime at its default 'auto' and the
effective backend is NIXL ("DEFAULT" resolves to NIXL when no legacy
backend env var is set), Kimi-K2.5 adopts the Python (v2) transceiver
instead of the C++ one. Explicit user settings and non-NIXL backends are
unaffected.

This lets the Kimi-K2.5 disaggregated tests keep backend "DEFAULT" without
hard-coding the transceiver runtime.

- modeling_kimi_k25.py: add get_preferred_transceiver_runtime() -> "PYTHON".
- test_modeling_kimi_k25.py: unit test asserting the preference.

Signed-off-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
Shixiaowei02 added a commit to Shixiaowei02/TensorRT-LLM that referenced this pull request Jul 16, 2026
Mirror the GPT-OSS approach (NVIDIA#16479): KimiK25ForConditionalGeneration now
declares "PYTHON" as its preferred KV-cache transceiver runtime. In
disaggregated serving, when the user leaves
cache_transceiver_config.transceiver_runtime at its default 'auto' and the
effective backend is NIXL ("DEFAULT" resolves to NIXL when no legacy
backend env var is set), Kimi-K2.5 adopts the Python (v2) transceiver
instead of the C++ one. Explicit user settings and non-NIXL backends are
unaffected.

This lets the Kimi-K2.5 disaggregated tests keep backend "DEFAULT" without
hard-coding the transceiver runtime.

- modeling_kimi_k25.py: add get_preferred_transceiver_runtime() -> "PYTHON".
- test_modeling_kimi_k25.py: unit test asserting the preference.

Signed-off-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
Shixiaowei02 added a commit to Shixiaowei02/TensorRT-LLM that referenced this pull request Jul 16, 2026
Mirror the GPT-OSS approach (NVIDIA#16479): KimiK25ForConditionalGeneration now
declares "PYTHON" as its preferred KV-cache transceiver runtime. In
disaggregated serving, when the user leaves
cache_transceiver_config.transceiver_runtime at its default 'auto' and the
effective backend is NIXL ("DEFAULT" resolves to NIXL when no legacy backend
env var is set), Kimi-K2.5 adopts the Python (v2) transceiver instead of the
C++ one. Explicit user settings and non-NIXL backends are unaffected.

This lets the Kimi-K2.5 disaggregated tests keep backend "DEFAULT" without
hard-coding the transceiver runtime.

- modeling_kimi_k25.py: add get_preferred_transceiver_runtime() -> "PYTHON".
- test_modeling_kimi_k25.py: unit test asserting the preference.
- Remove the redundant TestKimiK2::test_nvfp4 disaggregated accuracy test;
  Kimi disaggregated accuracy is covered by TestKimiK25, and Kimi-K2-Thinking
  accuracy stays covered by the aggregated tests in test_llm_api_pytorch.

Signed-off-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
Shixiaowei02 added a commit to Shixiaowei02/TensorRT-LLM that referenced this pull request Jul 16, 2026
Mirror the GPT-OSS approach (NVIDIA#16479): KimiK25ForConditionalGeneration now
declares "PYTHON" as its preferred KV-cache transceiver runtime. In
disaggregated serving, when the user leaves
cache_transceiver_config.transceiver_runtime at its default 'auto' and the
effective backend is NIXL ("DEFAULT" resolves to NIXL when no legacy backend
env var is set), Kimi-K2.5 adopts the Python (v2) transceiver instead of the
C++ one. Explicit user settings and non-NIXL backends are unaffected.

This lets the Kimi-K2.5 disaggregated tests keep backend "DEFAULT" without
hard-coding the transceiver runtime.

- modeling_kimi_k25.py: add get_preferred_transceiver_runtime() -> "PYTHON".
- test_modeling_kimi_k25.py: unit test asserting the preference.
- Remove the redundant TestKimiK2::test_nvfp4 disaggregated accuracy test;
  Kimi disaggregated accuracy is covered by TestKimiK25, and Kimi-K2-Thinking
  accuracy stays covered by the aggregated tests in test_llm_api_pytorch.

Signed-off-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
Shixiaowei02 added a commit to Shixiaowei02/TensorRT-LLM that referenced this pull request Jul 16, 2026
Mirror the GPT-OSS approach (NVIDIA#16479): KimiK25ForConditionalGeneration now
declares "PYTHON" as its preferred KV-cache transceiver runtime, so
disaggregated Kimi-K2.5 adopts the Python (v2) transceiver by default when the
user leaves transceiver_runtime at 'auto' and the effective backend is NIXL.
Explicit user settings and non-NIXL backends are unaffected.

- modeling_kimi_k25.py: add get_preferred_transceiver_runtime() -> "PYTHON".
- test_modeling_kimi_k25.py: unit test asserting the preference.
- TestKimiK25::test_nvfp4: set cache_transceiver_config explicitly to
  backend=NIXL + transceiver_runtime=PYTHON (ctx and gen). The disagg test
  harness injects TRTLLM_USE_UCX_KVCACHE=1 for any non-NIXL backend, so a
  "DEFAULT" backend would resolve to UCX and force the C++ transceiver; the
  test sets NIXL explicitly so it actually exercises the Python transceiver
  rather than relying on the model-default resolution.
- Remove the redundant TestKimiK2::test_nvfp4 disaggregated accuracy test;
  Kimi disaggregated accuracy is covered by TestKimiK25, and Kimi-K2-Thinking
  accuracy stays covered by the aggregated tests in test_llm_api_pytorch.

Signed-off-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
Shixiaowei02 added a commit to Shixiaowei02/TensorRT-LLM that referenced this pull request Jul 16, 2026
Mirror the GPT-OSS approach (NVIDIA#16479): KimiK25ForConditionalGeneration now
declares "PYTHON" as its preferred KV-cache transceiver runtime, so
disaggregated Kimi-K2.5 adopts the Python (v2) transceiver by default when the
user leaves transceiver_runtime at 'auto' and the effective backend is NIXL.
Explicit user settings and non-NIXL backends are unaffected.

- modeling_kimi_k25.py: add get_preferred_transceiver_runtime() -> "PYTHON".
- test_modeling_kimi_k25.py: unit test asserting the preference.
- TestKimiK25::test_nvfp4: set cache_transceiver_config explicitly to
  backend=NIXL + transceiver_runtime=PYTHON (ctx and gen). The disagg test
  harness injects TRTLLM_USE_UCX_KVCACHE=1 for any non-NIXL backend, so a
  "DEFAULT" backend would resolve to UCX and force the C++ transceiver; the
  test sets NIXL explicitly so it actually exercises the Python transceiver
  rather than relying on the model-default resolution.
- Remove the redundant TestKimiK2::test_nvfp4 disaggregated accuracy test;
  Kimi disaggregated accuracy is covered by TestKimiK25, and Kimi-K2-Thinking
  accuracy stays covered by the aggregated tests in test_llm_api_pytorch.

Signed-off-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
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PR_Github #59647 [ run ] completed with state FAILURE. Commit: a5219f6
/LLM/main/L0_MergeRequest_PR pipeline #48082 completed with status: 'FAILURE'

CI Report

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  • Please check the failed tests and fix your PR
  • If you cannot view the failures, ask the CI triggerer to share details
  • Once fixed, request an NVIDIA team member to trigger CI again

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PR_Github #59826 [ run ] triggered by Bot. Commit: a5219f6 Link to invocation

Shixiaowei02 added a commit to Shixiaowei02/TensorRT-LLM that referenced this pull request Jul 17, 2026
Mirror the GPT-OSS approach (NVIDIA#16479): KimiK25ForConditionalGeneration now
declares "PYTHON" as its preferred KV-cache transceiver runtime, so
disaggregated Kimi-K2.5 adopts the Python (v2) transceiver by default when the
user leaves transceiver_runtime at 'auto' and the effective backend is NIXL.
Explicit user settings and non-NIXL backends are unaffected.

- modeling_kimi_k25.py: add get_preferred_transceiver_runtime() -> "PYTHON".
- test_modeling_kimi_k25.py: unit test asserting the preference.
- TestKimiK25::test_nvfp4: set cache_transceiver_config explicitly to
  backend=NIXL + transceiver_runtime=PYTHON (ctx and gen). The disagg test
  harness injects TRTLLM_USE_UCX_KVCACHE=1 for any non-NIXL backend, so a
  "DEFAULT" backend would resolve to UCX and force the C++ transceiver; the
  test sets NIXL explicitly so it actually exercises the Python transceiver
  rather than relying on the model-default resolution.
- Remove the redundant TestKimiK2::test_nvfp4 disaggregated accuracy test;
  Kimi disaggregated accuracy is covered by TestKimiK25, and Kimi-K2-Thinking
  accuracy stays covered by the aggregated tests in test_llm_api_pytorch.

Signed-off-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
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/bot run --disable-fail-fast

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PR_Github #59863 [ run ] triggered by Bot. Commit: 92af2a5 Link to invocation

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PR_Github #59826 [ run ] completed with state ABORTED. Commit: a5219f6

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PR_Github #59863 [ run ] completed with state FAILURE. Commit: 92af2a5
/LLM/main/L0_MergeRequest_PR pipeline #48266 completed with status: 'FAILURE'

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  • Please check the failed tests and fix your PR
  • If you cannot view the failures, ask the CI triggerer to share details
  • Once fixed, request an NVIDIA team member to trigger CI again

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Shixiaowei02 added a commit to Shixiaowei02/TensorRT-LLM that referenced this pull request Jul 18, 2026
Mirror the GPT-OSS approach (NVIDIA#16479): KimiK25ForConditionalGeneration now
declares "PYTHON" as its preferred KV-cache transceiver runtime, so
disaggregated Kimi-K2.5 adopts the Python (v2) transceiver by default when the
user leaves transceiver_runtime at 'auto' and the effective backend is NIXL.
Explicit user settings and non-NIXL backends are unaffected.

- modeling_kimi_k25.py: add get_preferred_transceiver_runtime() -> "PYTHON".
- test_modeling_kimi_k25.py: unit test asserting the preference.
- TestKimiK25::test_nvfp4: set cache_transceiver_config explicitly to
  backend=NIXL + transceiver_runtime=PYTHON (ctx and gen). The disagg test
  harness injects TRTLLM_USE_UCX_KVCACHE=1 for any non-NIXL backend, so a
  "DEFAULT" backend would resolve to UCX and force the C++ transceiver; the
  test sets NIXL explicitly so it actually exercises the Python transceiver
  rather than relying on the model-default resolution.
- Remove the redundant TestKimiK2::test_nvfp4 disaggregated accuracy test;
  Kimi disaggregated accuracy is covered by TestKimiK25, and Kimi-K2-Thinking
  accuracy stays covered by the aggregated tests in test_llm_api_pytorch.

Signed-off-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
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nv-xtf commented Jul 19, 2026

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/bot run --disable-fail-fast

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PR_Github #60141 [ run ] triggered by Bot. Commit: 92af2a5 Link to invocation

Shixiaowei02 added a commit to Shixiaowei02/TensorRT-LLM that referenced this pull request Jul 19, 2026
Mirror the GPT-OSS approach (NVIDIA#16479): KimiK25ForConditionalGeneration now
declares "PYTHON" as its preferred KV-cache transceiver runtime, so
disaggregated Kimi-K2.5 adopts the Python (v2) transceiver by default when the
user leaves transceiver_runtime at 'auto' and the effective backend is NIXL.
Explicit user settings and non-NIXL backends are unaffected.

- modeling_kimi_k25.py: add get_preferred_transceiver_runtime() -> "PYTHON".
- test_modeling_kimi_k25.py: unit test asserting the preference.
- TestKimiK25::test_nvfp4: set cache_transceiver_config explicitly to
  backend=NIXL + transceiver_runtime=PYTHON (ctx and gen). The disagg test
  harness injects TRTLLM_USE_UCX_KVCACHE=1 for any non-NIXL backend, so a
  "DEFAULT" backend would resolve to UCX and force the C++ transceiver; the
  test sets NIXL explicitly so it actually exercises the Python transceiver
  rather than relying on the model-default resolution.
- Remove the redundant TestKimiK2::test_nvfp4 disaggregated accuracy test;
  Kimi disaggregated accuracy is covered by TestKimiK25, and Kimi-K2-Thinking
  accuracy stays covered by the aggregated tests in test_llm_api_pytorch.

Signed-off-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
@tensorrt-cicd

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PR_Github #60141 [ run ] completed with state SUCCESS. Commit: 92af2a5
/LLM/main/L0_MergeRequest_PR pipeline #48520 completed with status: 'FAILURE'

CI Report

⚠️ Action Required:

  • Please check the failed tests and fix your PR
  • If you cannot view the failures, ask the CI triggerer to share details
  • Once fixed, request an NVIDIA team member to trigger CI again

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PR_Github #60182 [ run ] triggered by Bot. Commit: 2df5cdc Link to invocation

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nv-xtf force-pushed the dev-tingfengx-gpt-oss-transceiver-v2-default branch from 2df5cdc to a9b554c Compare July 19, 2026 16:31
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/bot run --disable-fail-fast

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PR_Github #60184 [ run ] triggered by Bot. Commit: a9b554c Link to invocation

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PR_Github #60182 [ run ] completed with state ABORTED. Commit: 2df5cdc

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PR_Github #60184 [ run ] completed with state FAILURE. Commit: a9b554c
/LLM/main/L0_MergeRequest_PR pipeline #48557 completed with status: 'FAILURE'

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  • Please check the failed tests and fix your PR
  • If you cannot view the failures, ask the CI triggerer to share details
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PR_Github #60205 [ run ] triggered by Bot. Commit: a9b554c Link to invocation

Shixiaowei02 added a commit to Shixiaowei02/TensorRT-LLM that referenced this pull request Jul 20, 2026
Mirror the GPT-OSS approach (NVIDIA#16479): KimiK25ForConditionalGeneration now
declares "PYTHON" as its preferred KV-cache transceiver runtime, so
disaggregated Kimi-K2.5 adopts the Python (v2) transceiver by default when the
user leaves transceiver_runtime at 'auto' and the effective backend is NIXL.
Explicit user settings and non-NIXL backends are unaffected.

- modeling_kimi_k25.py: add get_preferred_transceiver_runtime() -> "PYTHON".
- test_modeling_kimi_k25.py: unit test asserting the preference.
- TestKimiK25::test_nvfp4: set cache_transceiver_config explicitly to
  backend=NIXL + transceiver_runtime=PYTHON (ctx and gen). The disagg test
  harness injects TRTLLM_USE_UCX_KVCACHE=1 for any non-NIXL backend, so a
  "DEFAULT" backend would resolve to UCX and force the C++ transceiver; the
  test sets NIXL explicitly so it actually exercises the Python transceiver
  rather than relying on the model-default resolution.
- Remove the redundant TestKimiK2::test_nvfp4 disaggregated accuracy test;
  Kimi disaggregated accuracy is covered by TestKimiK25, and Kimi-K2-Thinking
  accuracy stays covered by the aggregated tests in test_llm_api_pytorch.

Signed-off-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
Shixiaowei02 added a commit to Shixiaowei02/TensorRT-LLM that referenced this pull request Jul 20, 2026
Mirror the GPT-OSS approach (NVIDIA#16479): KimiK25ForConditionalGeneration now
declares "PYTHON" as its preferred KV-cache transceiver runtime, so
disaggregated Kimi-K2.5 adopts the Python (v2) transceiver by default when the
user leaves transceiver_runtime at 'auto' and the effective backend is NIXL.
Explicit user settings and non-NIXL backends are unaffected.

- modeling_kimi_k25.py: add get_preferred_transceiver_runtime() -> "PYTHON".
- test_modeling_kimi_k25.py: unit test asserting the preference.
- TestKimiK25::test_nvfp4: set cache_transceiver_config explicitly to
  backend=NIXL + transceiver_runtime=PYTHON (ctx and gen). The disagg test
  harness injects TRTLLM_USE_UCX_KVCACHE=1 for any non-NIXL backend, so a
  "DEFAULT" backend would resolve to UCX and force the C++ transceiver; the
  test sets NIXL explicitly so it actually exercises the Python transceiver
  rather than relying on the model-default resolution.
- Remove the redundant TestKimiK2::test_nvfp4 disaggregated accuracy test;
  Kimi disaggregated accuracy is covered by TestKimiK25, and Kimi-K2-Thinking
  accuracy stays covered by the aggregated tests in test_llm_api_pytorch.

Signed-off-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
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PR_Github #60205 [ run ] completed with state SUCCESS. Commit: a9b554c
/LLM/main/L0_MergeRequest_PR pipeline #48574 completed with status: 'FAILURE'

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nv-xtf added 2 commits July 20, 2026 11:10
Signed-off-by: Tingfeng Xian <289617005+nv-xtf@users.noreply.github.com>
Signed-off-by: Tingfeng Xian <289617005+nv-xtf@users.noreply.github.com>
@nv-xtf
nv-xtf force-pushed the dev-tingfengx-gpt-oss-transceiver-v2-default branch from a9b554c to 624016f Compare July 20, 2026 03:10
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PR_Github #60268 [ run ] triggered by Bot. Commit: 624016f Link to invocation

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PR_Github #60268 [ run ] completed with state SUCCESS. Commit: 624016f
/LLM/main/L0_MergeRequest_PR pipeline #48627 completed with status: 'FAILURE'

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PR_Github #60327 [ run ] triggered by Bot. Commit: 624016f Link to invocation

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PR_Github #60327 [ run ] completed with state FAILURE. Commit: 624016f
/LLM/main/L0_MergeRequest_PR pipeline #48671 completed with status: 'FAILURE'

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PR_Github #60381 [ run ] triggered by Bot. Commit: 624016f Link to invocation

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PR_Github #60381 [ run ] completed with state SUCCESS. Commit: 624016f
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nv-xtf merged commit 4e38fb8 into NVIDIA:main Jul 20, 2026
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4 participants