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wtdcode/Qwen3.8-Flash-Next-AWQ-W4A16
Qwen3.8-Flash-Next-AWQ-W4A16 is a machine learning model from wtdcode. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Quantized version of Qwen/Qwen3.8-Flash-Next.
Downloads · 30 days
4.6K
55% of all-time downloads
All-time downloads
8.4K
Public
Parameters
180B
181 GB on disk
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.safetensors181 GB · 100%
How the weights are stored.
I32121B · 67%
From the Hugging Face model README
Quantized version of Qwen/Qwen3.8-Flash-Next.
If you come here with older cards like A100, A6000 or 3090, consider using our vLLM fork which has served billions of tokens for frontier models with older GPUs.
Weight-only INT4 (symmetric, group size 128) via AWQ, stored in the compressed-tensors pack-quantized format.
Quantized (INT4 W4A16):
mlp.experts.{0..511}.{gate_proj, up_proj, down_proj} (≈123B of the 180B parameters)Kept in BF16 (not quantized):
embed_tokens) and lm_headlinear_attn.*)self_attn.{q,k,v,o}_proj) and its indexer (self_attn.indexer.*)*_hyper_connection.*, hyper_connection_mixer.*)mlp.gate), shared expert (mlp.shared_expert.*) and its gate (shared_expert_gate)ple.*)model.visual.*)mtp.*, in model_mtp.safetensors)