[Bugfix][Model] Fix PixtralForConditionalGeneration LoRA#36963
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[Bugfix][Model] Fix PixtralForConditionalGeneration LoRA#36963
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This was referenced Mar 13, 2026
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This pull request refactors the Pixtral vision encoder to utilize vLLM's parallel layers, which is a significant and well-executed change to enable tensor parallelism, quantization, and LoRA support. The code is mostly clean and consistent. I have one suggestion to improve the robustness of the weight loading logic for future compatibility with features like quantization.
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This pull request has merge conflicts that must be resolved before it can be |
Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>
ywang96
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Mar 30, 2026
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I don't think this will break the base model but FYI @patrickvonplaten |
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…t#36963) Signed-off-by: Jee Jee Li <pandaleefree@gmail.com> Co-authored-by: Roger Wang <hey@rogerw.io> Signed-off-by: neweyes <328719365@qq.com>
puririshi98
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Apr 7, 2026
…t#36963) Signed-off-by: Jee Jee Li <pandaleefree@gmail.com> Co-authored-by: Roger Wang <hey@rogerw.io> Signed-off-by: Rishi Puri <riship@nvidia.com>
mtparet
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…t#36963) Signed-off-by: Jee Jee Li <pandaleefree@gmail.com> Co-authored-by: Roger Wang <hey@rogerw.io>
iboiko-habana
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Apr 10, 2026
…xtral, MoE and Granite regressions (#1311) ## Summary This PR fixes a set of regressions introduced by recent upstream changes and observed in vLLM-Gaudi hourly validation. The branch now includes: - Pixtral HPUAttention projection path fix, - MoE dispatch and method override alignment updates for fused MoE and compressed tensors, - unit test updates to match the new MoE runner API usage, - fix hybrid model page size alignment for Granite 4.0-H. ## Related upstream PRs that introduced the regressions - vllm-project/vllm#37234 - vllm-project/vllm#35153 - vllm-project/vllm#36963 - vllm-project/vllm#38960 - vllm-project/vllm#35326 - vllm-project/vllm#37467 --------- Signed-off-by: Paweł Olejniczak <pawelx.olejniczak@intel.com>
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Purpose
Fix #34591
Test Plan
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Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.