doc: add bielik on support-matrix.md #6480
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📝 WalkthroughWalkthroughThe support matrix documentation for PyTorch backend models was updated by adding a new entry for the Changes
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docs/source/reference/support-matrix.md (4)
18-19: Modality tag exceeds new legendThe row for
LlavaLlamaModeluses the combined tagL + I + V, yet the legend (Lines 38-42) now explains letters individually. Consider either:
- Keeping the “+” notation but explicitly document in the legend that combinations are written as
L + I,L + I + V, …, or- Replace the cell content with a concatenated list, e.g.
L, I, V.This will avoid reader confusion.
21-24: DuplicateMistralForCausalLMarchitecture row – clarify variantLines 22-24 list two
MistralForCausalLMrows (“Bielik” and “Mistral”). Because the “Architecture” column is supposed to map 1-to-1 to a code class, having duplicates may mis-lead automation that parses the table.Consider one of:
-| `MistralForCausalLM` | Bielik | … | L | -| `MistralForCausalLM` | Mistral | … | L | +| `MistralForCausalLM` | Bielik (Mistral variant) | … | L |or split into separate architecture identifiers if they truly correspond to distinct subclasses.
29-34: Audio modality introduced – update legend examples
Phi4MMForCausalLMand the two Qwen-VL rows now include theA(Audio) modality, but there are no sample models in the list below demonstrating audio support. Adding at least one entry in the Multi-Modal Models section (e.g., Whisper or any speech model) with an A tag would help readers discover audio examples faster.
38-42: Legend wording could mention combination syntaxNow that multiple letters can be combined, a short sentence such as “Modality tags may be combined, e.g.,
L + I” would remove any ambiguity.
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docs/source/reference/support-matrix.md (2)
Learnt from: moraxu
PR: #6303
File: tests/integration/test_lists/qa/examples_test_list.txt:494-494
Timestamp: 2025-07-28T17:06:08.621Z
Learning: In TensorRT-LLM testing, it's common to have both CLI flow tests (test_cli_flow.py) and PyTorch API tests (test_llm_api_pytorch.py) for the same model. These serve different purposes: CLI flow tests validate the traditional command-line workflow, while PyTorch API tests validate the newer LLM API backend. Both are legitimate and should coexist.
Learnt from: yechank-nvidia
PR: #6254
File: tensorrt_llm/_torch/pyexecutor/model_engine.py:1201-1204
Timestamp: 2025-07-22T09:22:14.726Z
Learning: In TensorRT-LLM's multimodal processing pipeline, shared tensor recovery using from_shared_tensor() is only needed during the context phase. Generation requests reuse the already-recovered tensor data and only need to call strip_for_generation() to remove unnecessary multimodal data while preserving the recovered tensors. This avoids redundant tensor recovery operations during generation.
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Signed-off-by: Wanli Jiang <[email protected]>
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Signed-off-by: Wanli Jiang <[email protected]> Signed-off-by: Lanyu Liao <[email protected]>
Signed-off-by: Wanli Jiang <[email protected]>
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MistralForCausalLMwith the Bielik variant.Description
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