CAT-UT dense int4 checkpoints

This repository contains QPR artifacts for the dense, unpruned openbmb/MiniCPM5-2B model. The quantizer uses dense int4 weights with group size 128 and GPTQ calibration; it does not apply 2:4 pruning.

The refined candidate is dense-int4-distill2000-v2-best.qpr. The minicpm5-2b-dense-int4-g128.qpr file is the matching dense-int4 baseline. Each QPR file is about 1.30 GB and is stored with Git LFS. The candidate has not passed the current production acceptance gate; retain the baseline for production use.

Evaluation

The candidate was selected from a 2,000-update supervised refinement run starting at the dense-int4 baseline. It was selected at update 775 and independently reloaded before scoring.

Artifact	Wide code NLL	Wide chat NLL	Wide tool NLL
Dense int4 baseline	1.0654	5.1728	2.5966
Refined candidate	1.0323	5.0707	2.6200

In the current answer-only, 512-token bilingual chat check, an external pairwise judge gave 47 baseline wins, 49 candidate wins, and 4 ties across 100 prompts. The Chinese subset was 26/22/2 and the English subset 21/27/2 (baseline/candidate/tie). The candidate failed the Chinese non-regression gate. Candidate repetition was 0.0295, language mixing 0.0007, and truncation 0.2725; these automatic metrics passed their separate gates.

On 20 executable Python tasks, the baseline passed 14 and the candidate passed 15. Chinese results were 7/10 for both; English results were 7/10 and 8/10 respectively. A different, earlier chat protocol produced 39 baseline wins, 58 candidate wins, and 3 ties, but it does not determine release status under the current answer-only protocol.

Loading

QPR is a custom artifact format and is not loaded directly by transformers.from_pretrained. Use the qprsi source at commit 6d6ccc659d667f99a1a004fe31bb450e8c267d8f. The deployment README pins the base model to revision 12a3808a956f869c767195e9266b59c4d21d92e2 and provides an inference CLI, dependency lock, and SHA-256 verification.

After installing the base model and qprsi package, run:

qprsi-infer \
  --model /path/to/MiniCPM5-2B \
  --model-revision 12a3808a956f869c767195e9266b59c4d21d92e2 \
  --artifact /path/to/minicpm5-2b-dense-int4-g128.qpr \
  --expected-sha256 7b0ad4afe9eaf88ff6502cd7041487b8e1d833c00c40ec0eec2ac1eebfc35c02 \
  --language zh --prompt '请用两句话解释为什么雨后会有彩虹。'

For the candidate, replace the artifact and SHA-256 with dense-int4-distill2000-v2-best.qpr and 306d6475e1b0d07dc28a1b260186458a0494157a43a3c1028ba4fcc6af5b3615. Reproduce the current executable code check with:

python -m qprsi.code_eval \
  --model /path/to/MiniCPM5-2B \
  --artifacts /path/to/minicpm5-2b-dense-int4-g128.qpr /path/to/dense-int4-distill2000-v2-best.qpr \
  --answer-only --max-new-tokens 256 \
  --report code-eval.json

The QPR loader must use the same model architecture and tokenizer revision as the base model. The artifact is intended for research evaluation and is not a drop-in Transformers or vLLM checkpoint.

Training details

Supervised refinement used cached external response targets from an authorized OpenAI-compatible endpoint. The run used batch size 16, learning rate 3e-6, KL beta 0.05, last-block training, 1,075 executed updates, and wide-probe selection with a 1% code/tool regression gate. The selected update was 775. No API credentials are included in the artifacts or reports.

Limitations

• The chat comparison used one 100-prompt set and one external judge; the result is not a benchmark guarantee.
• Generation was capped at 512 new tokens. The answer-only prefill avoids spending that budget on the model's visible reasoning section.
• The 20-task code check and 100-prompt chat check are small samples. They do not establish a general quality gain.
• A separate 200-update rich-task pilot matched the baseline at 3/10 on that code check and lost its 100-prompt chat comparison (41 pilot wins, 51 baseline wins, 8 ties), so that experimental QPR is not included here.
• The QPR artifact does not contain the 5 GB BF16 base model. Download the base model separately and comply with its terms.

Licensing

The upstream openbmb/MiniCPM5-2B weights are released under Apache-2.0. The qprsi conversion and evaluation source is released under AGPL-3.0-or-later. The repository metadata currently declares MPL-2.0 for this CAT-UT repository; downstream users should review all applicable terms before redistribution.