---
license: apache-2.0
base_model: Qwen/Qwen2.5-VL-7B-Instruct
library_name: peft
tags:
- lora
- process-reward-model
- multimodal
- chart-reasoning
- em-prm
---
# EM-PRM v2 — `a2_support_qwen25_s0`

LoRA adapter for **Qwen/Qwen2.5-VL-7B-Instruct** from the paper *EM-PRM: Evidence-Mediated Process Rewards for Robust Multimodal Reasoning* (EM-PRM v2 experiment ladder).

A2 support checkpoint on Qwen2.5-VL-7B-Instruct (initialisation of the rung-6 arms).

## Training
- LoRA rank 64, alpha 128, dropout 0.05, target modules k_proj, layers.0.mlp.down_proj, layers.0.mlp.gate_proj, layers.0.mlp.up_proj, layers.1.mlp.down_proj, layers.1.mlp.gate_proj, layers.1.mlp.up_proj, layers.10.mlp.down_proj, layers.10.mlp.gate_proj, layers.10.mlp.up_proj, layers.11.mlp.down_proj, layers.11.mlp.gate_proj, layers.11.mlp.up_proj, layers.12.mlp.down_proj, layers.12.mlp.gate_proj, layers.12.mlp.up_proj, layers.13.mlp.down_proj, layers.13.mlp.gate_proj, layers.13.mlp.up_proj, layers.14.mlp.down_proj, layers.14.mlp.gate_proj, layers.14.mlp.up_proj, layers.15.mlp.down_proj, layers.15.mlp.gate_proj, layers.15.mlp.up_proj, layers.16.mlp.down_proj, layers.16.mlp.gate_proj, layers.16.mlp.up_proj, layers.17.mlp.down_proj, layers.17.mlp.gate_proj, layers.17.mlp.up_proj, layers.18.mlp.down_proj, layers.18.mlp.gate_proj, layers.18.mlp.up_proj, layers.19.mlp.down_proj, layers.19.mlp.gate_proj, layers.19.mlp.up_proj, layers.2.mlp.down_proj, layers.2.mlp.gate_proj, layers.2.mlp.up_proj, layers.20.mlp.down_proj, layers.20.mlp.gate_proj, layers.20.mlp.up_proj, layers.21.mlp.down_proj, layers.21.mlp.gate_proj, layers.21.mlp.up_proj, layers.22.mlp.down_proj, layers.22.mlp.gate_proj, layers.22.mlp.up_proj, layers.23.mlp.down_proj, layers.23.mlp.gate_proj, layers.23.mlp.up_proj, layers.24.mlp.down_proj, layers.24.mlp.gate_proj, layers.24.mlp.up_proj, layers.25.mlp.down_proj, layers.25.mlp.gate_proj, layers.25.mlp.up_proj, layers.26.mlp.down_proj, layers.26.mlp.gate_proj, layers.26.mlp.up_proj, layers.27.mlp.down_proj, layers.27.mlp.gate_proj, layers.27.mlp.up_proj, layers.3.mlp.down_proj, layers.3.mlp.gate_proj, layers.3.mlp.up_proj, layers.4.mlp.down_proj, layers.4.mlp.gate_proj, layers.4.mlp.up_proj, layers.5.mlp.down_proj, layers.5.mlp.gate_proj, layers.5.mlp.up_proj, layers.6.mlp.down_proj, layers.6.mlp.gate_proj, layers.6.mlp.up_proj, layers.7.mlp.down_proj, layers.7.mlp.gate_proj, layers.7.mlp.up_proj, layers.8.mlp.down_proj, layers.8.mlp.gate_proj, layers.8.mlp.up_proj, layers.9.mlp.down_proj, layers.9.mlp.gate_proj, layers.9.mlp.up_proj, o_proj, q_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/a2_support_qwen25_s0/`) and the paper bundle under `backdata/`.

## Load
```python
from transformers import AutoModelForImageTextToText, AutoProcessor
from peft import PeftModel
base = AutoModelForImageTextToText.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", dtype="bfloat16", device_map="cuda")
model = PeftModel.from_pretrained(base, "RESEARCH-EMPRM/emprm-v2-a2_support_qwen25_s0")
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-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.