license: apache-2.0 base_model: DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored pipeline_tag: text-generation library_name: transformers tags:

nvfp4
qwen3.8
qwen3.5
blackwell
mtp
speculative-decoding
turbo
twin-turbo
fable
cold-fusion
compressed-tensors language:
en
multilingual quantization_config: quant_method: compressed-tensors format: nvfp4-pack-quantized

Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-NVFP4

NVFP4 checkpoint of DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored. NVFP4 here means W4A16 with FP8 scales, group size 16, weight only. Linear layers are NVFP4, vision tower, linear attention path, lm_head and MTP head are kept in BF16 at the source. No calibration data was used. This matches the pattern used for other Qwen3.8 Cold Fusion NVFP4 builds.

HF repo: esatapedico/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-NVFP4 GGUF family: planned in esatapedico/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-NVFP4-GGUF (tier ladder over a shared NVFP4 backbone, see that repo card when live)

At a glance

Field	Value
Format	`compressed-tensors` `nvfp4-pack-quantized`
Quantization	W4A16, group size 16, FP8 E4M3 scales, weight only
Kept in BF16	vision tower, linear attention path, lm_head, embeddings, MTP head
Calibration	none
Base	`DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored` (Qwen3.8-27B, Apache 2.0)

Quantized set covers MLP on all 64 layers plus Q, K, V, O on the 16 full attention layers (256 NVFP4 tensors verified). The DeltaNet path stays BF16 in this checkpoint and is normalized to NVFP4 in the GGUF tier builds (448 tensor backbone).

Checkpoint

Single model.safetensors about 25 GB
config.json quantization_config.format=nvfp4-pack-quantized, quant_method=compressed-tensors, Qwen3_5ForCausalLM, 64 layers, hybrid GatedDeltaNet, 262144 context, MTP head
tokenizer.json intact, chat template intact
NVFP4 tensors verified via metadata checks, plus a vLLM smoke check (tensor parallel 2, coherent generation) before the GGUF tier builds

Provenance

Derivative of DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored (Apache 2.0) which itself derives from Qwen/Qwen3.8-27B. The TWIN-TURBO tune targets reduced thinking tokens with matched output quality. See the GGUF model card for full attribution once published.

License

apache-2.0

Card written with AI assistance.

Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-NVFP4

At a glance

Field

Value

Format

compressed-tensors nvfp4-pack-quantized

Quantization

W4A16, group size 16, FP8 E4M3 scales, weight only

Kept in BF16

vision tower, linear attention path, lm_head, embeddings, MTP head

Calibration

none

Base

DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored (Qwen3.8-27B, Apache 2.0)

Checkpoint

Single model.safetensors about 25 GB

config.json quantization_config.format=nvfp4-pack-quantized, quant_method=compressed-tensors, Qwen3_5ForCausalLM, 64 layers, hybrid GatedDeltaNet, 262144 context, MTP head

tokenizer.json intact, chat template intact

NVFP4 tensors verified via metadata checks, plus a vLLM smoke check (tensor parallel 2, coherent generation) before the GGUF tier builds