Qwen3.8-27B-DFlash2-NVFP4
Native NVFP4 (W4A16, group-16) requantization of the Qwen3.8-27B DFlash2 block drafter (5 Qwen3-style layers, 1.92B params). Built for the all-NVFP4 vLLM stack: NVFP4 target weights + NVFP4 draft weights + NVFP4 KV cache on SM120 (RTX 5090), verified at 262,144-token context with DFlash2 K=3.
Format
compressed-tensors nvfp4-pack-quantized:
- weights: float4,
strategy: tensor_group,group_size: 16,symmetric,dynamic: false,observer: memoryless_minmax,scale_dtype: torch.float8_e4m3fn - packed tensors:
weight_packed(uint8, [out, in/2]),weight_scale(fp8_e4m3fn, [out, in/16]),weight_global_scale(float32, divisor convention) - quantized:
self_attn.{q,k,v,o}_proj,mlp.{gate,up,down}_proj,fc(36 matrices total) - kept BF16:
kernel_projection(grouped convs),candidate_selector,hidden_projection, norms
Quantization
Round-to-nearest weight-only quantization (no calibration) from the BF16
checkpoint, using the release image's compressed_tensors 0.17.0
(compressors/nvfp4/helpers.pack_fp4_to_uint8). Reproducible with
scripts/build_nvfp4_draft.py (source: vLLM-sm12x DFlash2 release repo).
Checksums
model.safetensors(1361734592 bytes):db19f8496af66a5b8cc26c682a7c80a754af0fa62929e6b2f14aa14fe97d7126config.json:106a0e81a35acfbdfcd8d40786216f260a51e12e690ea820e9c05bc5d7ab7045
Usage (vLLM, SM120, all-NVFP4)
vllm serve <Qwen3.8-27B NVFP4 target> --speculative-config '{"method":"dflash","model":"<this repo>",'
' "num_speculative_tokens":3,"kv_cache_dtype":"nvfp4"}' --kv-cache-dtype nvfp4 --max-model-len 262144 --max-num-seqs 8 --compilation-config '{"cudagraph_mode":"FULL_AND_PIECEWISE",'
' "cudagraph_capture_sizes":[4,8,12,16,20,24,28,32]}'
Measured on the all-NVFP4 262k profile: 341,765-token NVFP4 KV pool (1.30x of 262k), greedy determinism byte-identical, acceptance mean 2.35 / 44.9% avg, and a 254,658-token request completing end-to-end.
The drafter shares the target's embeddings and lm_head (it ships neither), matching the upstream DFlash2 design. Apache-2.0, like the original drafter by Inco.
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