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llmfan46/Qwen3.6-27B-uncensored-heretic-v2 of Qwen/Qwen3.6-27B, with the MTP head and vision tower preserved.Qwen/Qwen3.6-27B (bf16)p-e-w)llmfan46/Qwen3.6-27B-uncensored-heretic-v2 — the heretic-derived bf16 abliteration of Qwen/Qwen3.6-27BQwen/Qwen3.6-27B (15 tensors, ~810 MB bf16) so SGLang/vLLM speculative decoding (--speculative-algo NEXTN) worksmodel.visual.* preserved in bf16 (333 tensors)eval_kld.py — per-token KLD averaged over 8 samples from neuralmagic/calibration (LLM split), max_seq=1024. Max sample KLD is the highest single-sample mean (catches outliers that the overall mean hides).| Comparison | Mean KLD (nats) | Max sample KLD | Samples | max_seq |
|---|---|---|---|---|
| vs Qwen3.6-27B base | 0.0425 | 0.1104 | 8 | 1024 |
Note: this pipeline always uploads the resulting checkpoint. Consult the KL divergence numbers above to judge whether the result is acceptable for your use case.
eval_ppl.py. Same tokenizer for every row so the numbers compare apples-to-apples.| Model | Perplexity | Tokens scored | Dataset | seq |
|---|---|---|---|---|
| Qwen3.6-27B base (bf16) | 7.3057 | 296907 | wikitext/wikitext-2-raw-v1/test | 2048 |
| this checkpoint | 7.4619 | 296907 | wikitext/wikitext-2-raw-v1/test | 2048 |
1from transformers import AutoModelForImageTextToText, AutoProcessor
2import torch
3
4repo = "huginnfork/Qwen3.6-27B-uncensored-heretic-v2-mtp"
5proc = AutoProcessor.from_pretrained(repo, trust_remote_code=True)
6model = AutoModelForImageTextToText.from_pretrained(
7 repo, dtype=torch.bfloat16, device_map="auto", trust_remote_code=True,
8)1vllm serve huginnfork/Qwen3.6-27B-uncensored-heretic-v2-mtp \
2 --trust-remote-code \
3 --gpu-memory-utilization 0.85 \
4 --max-model-len 8192 \
5 --speculative-config '{"method":"qwen3_5_mtp","num_speculative_tokens":1}'