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[Doc] JoyVL recipe: describe model as retrained Qwen3-VL, not vanilla #4622

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[Doc] JoyVL recipe: describe model as retrained Qwen3-VL, not vanilla #4622

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9 changes: 5 additions & 4 deletions recipes/JD/JoyAI-VL-Interaction.md
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Expand Up @@ -5,7 +5,7 @@
## Summary

- Vendor: JD (Joy Future Academy)
- Model: [`jdopensource/JoyAI-VL-Interaction-Preview`](https://huggingface.co/jdopensource/JoyAI-VL-Interaction-Preview) (8B, vanilla Qwen3-VL weights)
- Model: [`jdopensource/JoyAI-VL-Interaction-Preview`](https://huggingface.co/jdopensource/JoyAI-VL-Interaction-Preview) (8B, Qwen3-VL architecture with weights retrained for streaming interaction)
- Task: Per-tick proactive interaction over a live video stream — the model decides
on its own each second to speak, stay silent, or delegate a hard question
- Mode: Online serving — an OpenAI-compatible interaction orchestrator in front of
Expand Down Expand Up @@ -33,7 +33,7 @@ framework internals and how to add another full-duplex model, see
From repository root:

```bash
# 1. Serve the model (plain `vllm serve`, NOT --omni; it is vanilla Qwen3-VL).
# 1. Serve the model (plain `vllm serve`, NOT --omni; it uses the Qwen3-VL architecture).
# The image limit must cover the short-term frame window (chunk_frames, default 100 = T_s);
# prefix caching keeps the accumulating window cheap. Lower both for smaller GPUs.
vllm serve jdopensource/JoyAI-VL-Interaction-Preview \
Expand Down Expand Up @@ -176,8 +176,9 @@ the audio-track caveat.

## Notes

- `--omni` is **not** used: the model is standard Qwen3-VL, so stock `vllm serve`
runs the forward pass; this recipe only adds the interaction/serving layer.
- `--omni` is **not** used: the model keeps the Qwen3-VL architecture (only the
weights are retrained), so stock `vllm serve` runs the forward pass; this recipe
only adds the interaction/serving layer.
- On a host without `nvcc` / `ninja`, `vllm serve` of the 8B can crash engine-core in the
FlashInfer sampler JIT (`FileNotFoundError: 'ninja'`) during `profile_run`. Set
`VLLM_USE_FLASHINFER_SAMPLER=0` (or install `ninja`) to work around it.
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