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ngqtrung
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Model Details
Model Provider
ngqtrung
Model Tree
Qwen/Qwen3-VL-8B-Instruct
Input Modalities
Output Modalities
Supported Functionality
OMR 6-image inline validation (mmmu val, mathvista testmini, mathverse testmini Text-Dominant, wemath testmini, charxiv reasoning-qa, dynamath test). overall-6 = unweighted mean. Metric = accuracy.
Keeper = global_step_25 (peak):
| metric | overall-6 | mmmu | mathvista | mathverse | wemath | charxiv | dynamath |
|---|---|---|---|---|---|---|---|
| baseline peak @25 | 0.6681 | 0.6311 | 0.8019 | 0.8404 | 0.7437 | 0.3930 | 0.5986 |
| stock Qwen3-VL-8B ckpt-0 | 0.659 | 0.629 | 0.811 | 0.824 | 0.723 | 0.396 | 0.570 |
| baseline @125 (collapse) | 0.4994 | 0.5021 | 0.6796 | 0.6995 | 0.6184 | 0.2010 | 0.2958 |
Full trajectory (overall-6): 0.668 @25 → 0.644 @50 → 0.620 @75 → 0.620 @100 → 0.499 @125 (monotone decline; the step-125 drop is catastrophic — charxiv halved, dynamath collapsed). Run died to a vLLM deepstack crash after step 125, but the model was already degrading.
Qwen/Qwen3-VL-8B-Instruct (cold-start, resume_mode=auto).ngquangtrung57/verl@videorl-mods. Fully-async GRPO: FSDP2 trainer + vLLM rollouter.score = 0.8·accuracy + 0.2·format (FORMAT_WEIGHT=0.2, FORMAT_MIN_THINK_CHARS=100). No KL penalty.omr-8b-grpo-ppexplore).Project verl_fully_async (entity quangtrung5705-nanyang-technological-university-singapore):
https://wandb.ai/quangtrung5705-nanyang-technological-university-singapore/verl_fully_async/runs/gkiiopep
Research checkpoint — the cold-start GRPO baseline in a controlled exploration study. Useful mainly as the A/B reference for omr-8b-grpo-ppexplore (the winner). It only marginally beats the zero-shot base (0.668 vs 0.659) at its peak and is unstable (collapses to 0.499 if trained past ~step 100); for actual use prefer the ppexplore checkpoint. Math / visual-reasoning image QA, <think>…</think> then-answer format. No safety/RLHF alignment beyond the base.
python
from transformers import AutoModelForImageTextToText, AutoProcessorfrom PIL import Image model_id = "ngqtrung/omr-8b-grpo-base"model = AutoModelForImageTextToText.from_pretrained(model_id, dtype="auto", device_map="auto")processor = AutoProcessor.from_pretrained(model_id) messages = [{ "role": "user", "content": [ {"type": "image", "image": Image.open("problem.png")}, {"type": "text", "text": "Solve the problem. Think step by step inside <think>...</think>, then give the final answer."}, ],}]inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=2048)print(processor.batch_decode(out[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0])ngquangtrung57/verl@videorl-mods; fully-async GRPO (FSDP2 + vLLM).docs/experiments_summary_8b.md).ppo_mini_batch_size=16 × require_batches=4 × rollout.n=8 = 512 trajectories/step.1e-6, warmup 25 steps; total_epochs=2; clip_ratio 0.2 / 0.3 (clip_c=10.0); max_prompt_length=2048, max_response_length=16384; enforce_eager=true; gpu_memory_utilization=0.75; staleness 0.5.