library_name: transformers license: apache-2.0 base_model: orcarouter/Qwen3.8-27B-Uncensored base_model_relation: quantized pipeline_tag: image-text-to-text tags:
- qwen3_5
- int8
- autoround
- w8a16
Qwen3.8-27B-Uncensored INT8 AutoRound (W8A16, linear attention BF16, group size 128)
INT8 weight-only quantization of orcarouter/Qwen3.8-27B-Uncensored
with AutoRound (SignRound), following the recipe of
Minachist/Qwen3.8-27B-INT8-AutoRound branch linear-attn-bf16-gs128,
with two changes: linear attention is excluded from tuning (not swapped back to BF16 after the fact), and 500 iters instead of 250.
| Tensors | Precision |
|---|---|
self_attn.{q,k,v,o}_proj (16 full-attention layers), mlp.{gate,up,down}_proj (64 layers), MTP block projections | INT8 symmetric, group_size 128 |
linear_attn.{in_proj_qkv,in_proj_z,out_proj,in_proj_a,in_proj_b} (48 GDN layers), embed_tokens, lm_head, mtp.fc, norms, vision tower | BF16 |
263 INT8 linears / 354 BF16 linears. Format: auto_round:auto_gptq packing (vLLM loads it via GPTQ-Marlin with BF16 activations).
Recipe
AutoRound main @ b9f3d0079d014c73a1ff009800c597b9bc3f2a36 (version string 0.15.0), transformers 5.15.1, torch 2.13.0+cu130, one RTX PRO 6000 Blackwell.
scheme="W8A16" (bits 8, group_size 128, sym), iters=500, nsamples=1024, seqlen=2048, batch_size=4, gradient_accumulate_steps=2, low_gpu_mem_usage=False, seed=42.
Calibration: 256 samples built from NeelNanda/pile-10k + 768 from codeparrot/github-code-clean (documents concatenated so every sample is >= 2048 tokens, then truncated to 2048).
Every layer is named in full in layer_config (avoids AutoRound's shared-dict regex aliasing bug). Tuning took 1.26 h.
KL divergence vs the BF16 source
Teacher-forced top-24 logprobs on one 128,000-token wikitext-103 stream (rows 100k+ of the train split), one sequence, BF16 KV cache, vLLM 0.27.1, KL(P_bf16 || Q_int8) in nats over the truncated top-24.
These numbers are only comparable to other models scored with the same script, stream and teacher.
| depth | n | KL mean | KL p50 | KL p99 | top-1 agreement | ΔNLL |
|---|---|---|---|---|---|---|
| 0k-4k | 3,999 | 0.00189 | 0.00056 | 0.0239 | 97.67% | +0.0038 |
| 4k-16k | 12,000 | 0.00363 | 0.00078 | 0.0338 | 97.51% | +0.0011 |
| 16k-48k | 32,000 | 0.00264 | 0.00085 | 0.0293 | 97.22% | +0.0019 |
| 48k-128k | 80,000 | 0.00320 | 0.00088 | 0.0338 | 97.28% | +0.0022 |
Own NLL: BF16 1.8244, INT8 1.8265. For reference, the same script on Qwen/Qwen3.8-27B gives FP8 (Qwen/Qwen3.8-27B-FP8) KL 0.0048 / top-1 96.5% and Minachist's INT8 0.0029 / 97.2%.
Serving
vllm serve <this-repo> --tensor-parallel-size 2 --max-model-len 262144 --trust-remote-code
Tested with vLLM 0.27.1 (Using MarlinLinearKernel for AutoGPTQLinearMethod). MTP speculative decoding: --speculative-config '{"method":"mtp","num_speculative_tokens":3}'.