01 / IMPLEMENTATION & CONTRIBUTION
What the work involves
Local changes adapt the upstream chen-jan pipeline to Qwen3.5 chat and processor conventions, distinguish model-loading paths, and add an environment-specific dependency profile. The original algorithms and training workflow remain upstream work.
Technical depth
Typed chat content and processor normalization handle Qwen3.5 multimodal architecture conventions. Loader/generation paths distinguish FastModel from FastLanguageModel and disable the vLLM fast path for this architecture. Pipeline stages combine reward training, rejection sampling, supervised fine-tuning, and evaluation.
The project family
A_Share/VTA02 / RESULTS
What came out of it
Compatibility code spans generation and staged GRPO/LoRA/SFT workflows. A completed training run or forecasting improvement has not been established.
03 / SUPPORTING EVIDENCE
Follow the source
Implementation notes, project records, and supporting artifacts.
Source context & project scope
Upstream algorithm, training pipeline, and paper authorship must be credited separately.
README marketing about improved forecasting is not a verified result of this adaptation.
Local compatibility branches are environment-specific and not a universal statement that Qwen3.5 cannot run on vLLM.
No predictive advantage, live trading value, or training completion is established.
Qwen3.5 detection, typed messages, dataset normalization, model loading and inference compatibility.
SOURCE · 2026-09-17Pinned local CUDA/PyTorch/Transformers profile and explicit Unsloth inference route.
SOURCE · 2026-09-17Training-stage architecture, with outcome claims requiring separate validation.
SOURCE · 2026-09-17Scoped current modifications against the clean upstream import; no candidate-authored commit attribution found for this path.
SOURCE · 2026-09-17