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MooreMuaMu/qwen35-27b-ancient-rl-r32-step200

sourceHugging Faceapache-2.0updated 1mo agoView on Hugging Face

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qwen35-27b-ancient-rl-r32-step200

This repository contains the full merged bfloat16 model for the rank-32 GRPO checkpoint at step 200. It is not an adapter-only upload: the LoRA adapter has been merged into the Qwen3.5-27B ancient-language stage-2 base model, and the repository includes the safetensors shards plus tokenizer/processor/chat-template files needed for direct transformers loading.

Adapter-only sibling: MooreMuaMu/qwen35-27b-ancient-rl-r32-step200-lora.

Intended Use

python

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "MooreMuaMu/qwen35-27b-ancient-rl-r32-step200"
tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)

Training Source

—Base model: /blob/models/qwen35-27b-ancient-stage2/checkpoint-227-merged
—LoRA checkpoint: /blob/experiments/ancient-swift-rl-stage2/qwen35_27b_ckpt227_uy_mn_bo_ta_full_r32_lr1e4/v0-20260811-070520/checkpoint-200
—LoRA rank/alpha: 32/32
—Merge dtype: bfloat16
—Serialization: safetensors, 5GB max shard size

Evaluation Summary

Evaluation used the independent six-bucket held-out subset: 300 prompts, 50 per bucket, thinking enabled, temperature 0.7, top-p 0.95, n=1, seed 42. Deltas are prompt-paired against the same base-model generations where applicable. Bootstrap confidence intervals use 10,000 paired samples.

Overall semantic metrics:

Metric	Base	Step 200	Delta	95% CI
Exact	0.3367	0.3100	-0.0267	[-0.0700, +0.0133]
Char-F1	0.5708	0.5758	+0.0050	[-0.0320, +0.0431]
BERTScore-F1	0.7627	0.7901	+0.0274	[-0.0010, +0.0571]
Semantic composite	0.6061	0.6139	+0.0078	[-0.0255, +0.0415]
Has answer	0.9400	0.9867	+0.0467	[+0.0167, +0.0767]

SacreBLEU2 answer-extraction metrics use sacrebleu.metrics.BLEU(tokenize="zh", max_ngram_order=2, smooth_method="exp", effective_order=True) after extracting text inside <ANS>...</ANS> and discarding any prefix before </think>.

Metric	Base	Step 200	Delta
Corpus BLEU-2 (%)	34.59	37.80	+3.21 pp
Sentence BLEU-2 mean (%)	48.42	47.59	-0.82 pp
Nonempty extracted answer	93.7%	98.7%	+5.0 pp

Bucket-level SacreBLEU2:

Bucket	Corpus BLEU-2 base -> step200	Delta	Sentence BLEU-2 base -> step200	Delta	Step200 nonempty
Tibetan annotation	65.98 -> 59.98	-6.00 pp	74.86 -> 66.85	-8.01 pp	96.0%
Tibetan translation	14.16 -> 21.22	+7.06 pp	14.20 -> 18.87	+4.67 pp	100.0%
Traditional Mongolian annotation	48.69 -> 54.63	+5.94 pp	48.83 -> 47.03	-1.79 pp	98.0%
Traditional Mongolian translation	33.79 -> 30.38	-3.41 pp	31.77 -> 32.50	+0.73 pp	100.0%
Uyghur annotation	81.43 -> 89.15	+7.72 pp	85.26 -> 89.94	+4.68 pp	98.0%
Uyghur translation	39.48 -> 30.69	-8.79 pp	35.57 -> 30.35	-5.21 pp	100.0%

Decision note: step 200 is an exploratory checkpoint. It improves answer presence significantly and has positive BERTScore/SacreBLEU2 corpus movement, but aggregate semantic confidence intervals still cross zero and translation buckets are mixed.