Qwen3.5-27B Ancient RL R32 Step 250 Merged
This repository contains full merged model weights. It is not a LoRA-only adapter repo.
Users can load this repo directly with a Transformers version that supports qwen3_5 / Qwen3_5ForConditionalGeneration.
The corresponding adapter-only repo is MooreMuaMu/qwen35-27b-ancient-rl-r32-step250-lora.
Usage
import torch
from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration
repo_id = "MooreMuaMu/qwen35-27b-ancient-rl-r32-step250"
model = Qwen3_5ForConditionalGeneration.from_pretrained(
repo_id,
dtype=torch.bfloat16,
device_map="auto",
)
processor = AutoProcessor.from_pretrained(repo_id)
For text-only serving, use the same chat template/tokenizer files included in this repo.
Selection status
Recommended checkpoint from the Ancient Swift RL Stage 2 R32 run. In the held-out 300-prompt small eval, step250 was the best checkpoint and showed statistically significant improvement versus the base model.
Do not use later checkpoints from this run (step300, step350, step400, step450) for model selection: held-out quality degraded at step300 and collapsed after step350.
Training run
- Base model:
/blob/models/qwen35-27b-ancient-stage2/checkpoint-227-merged - Source LoRA checkpoint:
/blob/experiments/ancient-swift-rl-stage2/qwen35_27b_ckpt227_uy_mn_bo_ta_full_r32_lr1e4/v0-20260811-070520/checkpoint-250 - LoRA rank/alpha:
32/32 - LoRA dropout:
0.05 - LoRA target modules: all linear target modules in the Qwen3.5 language model
- RL algorithm: GRPO
- LR:
1e-4 - GRPO beta:
0.001 - Training data: Uyghur, traditional Mongolian, Tibetan translation + annotation
Held-out small eval versus base
300 prompts, 50 per bucket, thinking on, temperature 0.7, top-p 0.95, n=1, seed 42.
Prompt-level paired bootstrap: 10,000 samples, seed 20260811.
| Metric | Delta vs base |
|---|---|
| Exact | +0.0400 CI [+0.0033,+0.0800] |
| Char-F1 | +0.0539 CI [+0.0160,+0.0910] |
| BERTScore-F1 | +0.0631 CI [+0.0330,+0.0942] |
| Semantic composite | +0.0555 CI [+0.0222,+0.0880] |
| Has ANS | +0.0667 CI [+0.0333,+0.1000] |
| Adapter has_ans | 0.9867 |
| SacreBLEU-2 corpus delta | +7.98 pp |
| SacreBLEU-2 sentence mean delta | +4.21 pp |
The SacreBLEU-2 numbers use sacrebleu.metrics.bleu.BLEU(tokenize="zh", max_ngram_order=2, smooth_method="exp", effective_order=True) after extracting <ANS>...</ANS> from the completion.
Weight files
-
Total parameters in index metadata:
27,356,728,560 -
Total tensor bytes in index metadata:
54,713,457,120 -
Safetensors shards:
12 -
model-00001-of-00012.safetensors: 2,542,796,928 bytes -
model-00002-of-00012.safetensors: 4,842,451,920 bytes -
model-00003-of-00012.safetensors: 4,965,227,944 bytes -
model-00004-of-00012.safetensors: 4,912,819,264 bytes -
model-00005-of-00012.safetensors: 4,986,198,544 bytes -
model-00006-of-00012.safetensors: 4,912,819,320 bytes -
model-00007-of-00012.safetensors: 4,932,703,272 bytes -
model-00008-of-00012.safetensors: 4,966,314,576 bytes -
model-00009-of-00012.safetensors: 4,964,162,248 bytes -
model-00010-of-00012.safetensors: 4,933,789,824 bytes -
model-00011-of-00012.safetensors: 4,965,228,032 bytes -
model-00012-of-00012.safetensors: 2,789,094,896 bytes
Notes
This is an experimental merged checkpoint. Run your own task-specific evaluation before production use.