AI is happening. The future is updating.Qwen3.8-Flash-Next-DERISKED-W4A16-AWQ
A **W4A16 (AWQ)** quantization of `Blackfrost-AI/Qwen3.8-Flash-Next-DERISKED-BF16`, an abliterated ("derisked") variant of Qwen's Qwen3.8-Flash-Next. Multimodal (image-text-to-text), MTP speculative-decode head and PLE n-gram table preserved. Built to run the 80B-class Flash-Next MoE on 2× AMD MI210 (gfx90a) at long context. - **Format:** `compressed-tensors`, **W4A16** — 4-bit weights, group size **128**, symmetric, no zero-point (loads on vLLM's `TritonW4A16` + `fused_moe_kernel_gptq_awq` fast paths). - **Method:** **AWQ** (activation-aware). Smoothing is restricted to the mathematically-exact `up_proj → down_proj` pairs. The standard `norm → q/k/v` and `norm → gate/up` smoothings are deliberately **excluded**: this architecture uses hyper-connections with a data-dependent (sigmoid) gate, so folding a smoothing scale into the norm would change the gate non-linearly and corrupt the weights. - **Quantized:** routed experts (gate/up/down) and full-attention `q/k/v/o`. - **Kept in bf16** (matching the reference recipe): the GDN linear-attention layers (recurrent state — error compounds along the sequence), the shared expert, the QSA indexer, the router gates, hyper-connections, norms, `embed_tokens`, `lm_head`, the **MTP** speculative-decode head, and the **PLE** n-gram embedding table.
A **W4A16 (AWQ)** quantization of `Blackfrost-AI/Qwen3.8-Flash-Next-DERISKED-BF16`, an abliterated ("derisked") variant of Qwen's Qwen3.8-Flash-Next. Multimodal (image-text-to-text), MTP speculative-decode head and PLE n-gram table preserved. Built to run the 80B-class Flash-Next MoE on 2× AMD MI210 (gfx90a) at long context. - **Format:** `compressed-tensors`, **W4A16** — 4-bit weights, group size **128**, symmetric, no zero-point (loads on vLLM's `TritonW4A16` + `fused_moe_kernel_gptq_awq` fast paths). - **Method:** **AWQ** (activation-aware). Smoothing is restricted to the mathematically-exact `up_proj → down_proj` pairs. The standard `norm → q/k/v` and `norm → gate/up` smoothings are deliberately **excluded**: this architecture uses hyper-connections with a data-dependent (sigmoid) gate, so folding a smoothing scale into the norm would change the gate non-linearly and corrupt the weights. - **Quantized:** routed experts (gate/up/down) and full-attention `q/k/v/o`. - **Kept in bf16** (matching the reference recipe): the GDN linear-attention layers (recurrent state — error compounds along the sequence), the shared expert, the QSA indexer, the router gates, hyper-connections, norms, `embed_tokens`, `lm_head`, the **MTP** speculative-decode head, and the **PLE** n-gram embedding table.