NVIDIA / NVIDIA/TensorRT-Model-Connect
Feature: Add Qwen3-VL-8B-Instruct to the qwen_vl family
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 254
- Forks
- 58
- Avg merge
- 1d 8h
- Merged PRs (30d)
- 223
Description
Summary
Extend the qwen_vl family's existing Qwen/Qwen3-VL-2B-Instruct support to
Qwen/Qwen3-VL-8B-Instruct.
Why
Both checkpoints share the same architecture class
(Qwen3VLForConditionalGeneration, model_type: qwen3_vl) and only differ in
scale:
| 2B (supported) | 8B (this issue) | |
|---|---|---|
| Text layers / hidden / heads | 28 / 2048 / 16 | 36 / 4096 / 32 |
| Vision depth / hidden | 24 / 1024 | 27 / 1152 |
families/qwen_vl/config.py reads these dimensions from the checkpoint's own
config.json rather than hardcoding the 2B shape, so this is expected to be a
scale-up within the existing family rather than a new architecture.
Apache-2.0, ~19.7M downloads on Hugging Face.
Plan
- Add a manifest + test case for
Qwen/Qwen3-VL-8B-Instructunder
families/qwen_vl/tests/manifests/. - Build and run the real GPU E2E (native
generate/ VL-inference CLI path)
against the pinned checkpoint revision. - If the 8B checkpoint needs tensor-parallel to fit comfortably, evaluate
qwen_vl's existing TP builder path (already used for
Qwen/Qwen2.5-VL-3B-Instruct). - No shared-file changes expected (no new task type, no new CLI surface) —
should stay entirely insidefamilies/qwen_vl/.
Non-goals
Quantized precisions, additional Qwen3-VL sizes beyond 8B, streaming
generation.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start in families/qwen_vl/config.py and inspect the existing manifest and tests under families/qwen_vl/tests/manifests/, especially the Qwen3-VL-2B-Instruct case. Add the 8B manifest and test, then build and run the pinned-checkpoint GPU E2E through the native generate or VL-inference CLI path. If it does not fit comfortably, evaluate the existing tensor-parallel builder path; done means the real E2E passes.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, testing-qa
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Active
- Clarity
- Mostly clear
- Newbie friendliness
- 68/100