RESEARCH-EMPRM/emprm-v2-a3_claims_s0

EM-PRM v2 — a3_claims_s0

LoRA adapter for Qwen/Qwen3-VL-8B-Instruct from the paper EM-PRM: Evidence-Mediated Process Rewards for Robust Multimodal Reasoning (EM-PRM v2 experiment ladder).

A3 claims-stage checkpoint (see planning/V2_PLAN.md in the dataset mirror for its pre-registration).

Training

• LoRA rank 64, alpha 128, dropout 0.05, target modules down_proj, gate_proj, k_proj, o_proj, q_proj, up_proj, v_proj; vision tower frozen; bfloat16.
• Seed 0, learning rate 5e-05, effective batch 2×4, one epoch.
• Training data, pair sets and every gate artifact are in the mirror RESEARCH-EMPRM/emprm-v2 (dataset repo; results/runs_v2/train/a3_claims_s0/) and the paper bundle under backdata/.

Load

from transformers import AutoModelForImageTextToText, AutoProcessor
from peft import PeftModel
base = AutoModelForImageTextToText.from_pretrained("Qwen/Qwen3-VL-8B-Instruct", dtype="bfloat16", device_map="cuda")
model = PeftModel.from_pretrained(base, "RESEARCH-EMPRM/emprm-v2-a3_claims_s0")
processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-8B-Instruct")

adapter_config.json records the local path the adapter was trained from; pass the base model explicitly as above. Scoring prompts (bank extraction, claim extraction, claim support, ranking) are the ones in work/scripts/eval_bon.py of the mirror.

Provenance

Trained in the EM-PRM v2 repository; every number quoted in the paper is traceable to planning/V2_PLAN.md and the generated tables in the mirror.