---
base_model: openbmb/MiniCPM5-2B
library_name: peft
license: apache-2.0
pipeline_tag: text-generation
tags:
  - lora
  - peft
  - open-jev
  - classification
  - logprob
  - minicpm
  - adapters
datasets:
  - ZefanCai/Open-Jev-v1.1
---

# Open-Jev Phase-1 LoRA — MiniCPM5-2B

PEFT LoRA adapter for [openbmb/MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B), fine-tuned for **closed-set first-token scoring** on Open-Jev Choice / Noul / Score fields (Bev/Jev-style: one forward pass, candidate-token logits; not free-form generation).

## Results (frozen panel, never in train)

| Slice | Metric | Zero-shot | This LoRA | Δ |
|-------|--------|-----------|-----------|---|
| Test n=900 | overall accuracy | 0.467 | **0.702** | +23.6 pp |
| Test | Noul recall | 0.118 | **0.735** | +61.8 pp |
| OOD n=300 | overall accuracy | 0.473 | **0.710** | +23.7 pp |

Scorekeeper planted gold; see local `results/openjev-phase1/RUN_CARD.md` for full tables.

## Recipe

- **Base:** `openbmb/MiniCPM5-2B`
- **Train:** stratified Open-Jev subset, n=6000 (2000 Choice / 2000 Noul / 2000 Score); dataset rev `10ad6888333fa97f8c948192797bad3de3040802`
- **Objective:** causal LM loss **only** on the gold index-surrogate token after `Answer: `
- **LoRA:** r=16, α=32, dropout=0.05; targets `q/k/v/o/gate/up/down_proj`
- **Train:** 1 epoch, lr=2e-4, effective batch 16, max length 2048, bfloat16
- **Hardware:** Modal A10G (~59.5 min wall, ~15.2 GB peak VRAM)
- **Seed:** 20260924

## Load

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_id = "openbmb/MiniCPM5-2B"
adapter_id = "nicolasembleton/openjev-minicpm5-2b-lora-phase1"

tok = AutoTokenizer.from_pretrained(base_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(base_id, trust_remote_code=True, torch_dtype="auto")
model = PeftModel.from_pretrained(model, adapter_id)
```

For Open-Jev TypeSafe-compatible scoring, point `OPENJEV_ADAPTER_PATH` at this adapter (or a local checkout) on top of the same base.

## Browser / WebGPU note

This repo is the **PEFT adapter only**. In-browser Transformers.js needs a **merged** weights export (and ideally quantized ONNX). That path is separate from this Hub upload.

## Intended use

Research and demos of closed-set Choice / Noul / Score scoring on Open-Jev-style prompts. Not a general chat model. Do not treat closed-menu argmax as ground truth without an external scorekeeper.

## License

Adapter weights follow the base model license terms for derivatives of MiniCPM5-2B (Apache-2.0 style redistribution where permitted by the base). Training data: Open-Jev v1.1.