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MooreMuaMu/qwen35-27b-ancient-rl-r32-step250
qwen35-27b-ancient-rl-r32-step250 is a text generation model from MooreMuaMu. Use it when you need the model to write or continue text. It is set up for transformers.
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 qwen35 / Qwen35ForConditionalGeneration.
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From the Hugging Face model README
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.
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.
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.
/blob/models/qwen35-27b-ancient-stage2/checkpoint-227-merged/blob/experiments/ancient-swift-rl-stage2/qwen35_27b_ckpt227_uy_mn_bo_ta_full_r32_lr1e4/v0-20260811-070520/checkpoint-25032/320.051e-40.001300 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.
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
This is an experimental merged checkpoint. Run your own task-specific evaluation before production use.