MODEL PROFILE

Qwen3.8-27B-AWQ-INT4-FP8KV

abhishekchohan · Qwen3.8-27B-AWQ-INT4-FP8KV

The scales were measured on the quantized model itself, not on the BF16 base, because activation-aware smoothing and int4 rounding change the K/V activations the served model actually writes to cache. Calibration replayed the exact same sequences that were used to calibrate the AWQ model: **112 packed sequences × 262,144 tokens (~29.4M tokens)** from the same Nemotron Post-Training v3 SFT blend (instruction-following chat, math, science, agentic code, multilingual), same shuffle seed, rendered with thinking enabled, so the measured KV distribution matches the deployment regime the weights were tuned for, at the exact context length the model serves at. While the model processed those sequences, we tracked the running maximum absolute value of the K states (after QK-norm) and V states each of the 16 global attention layers writes to the cache. Each scale is `amax / 448` (the fp8-e4m3 representable maximum), symmetric, per tensor, so no cached value can clip. The result is 32 per-layer scalars (`k_scale` / `v_scale`). Only the 16 global attention layers accumulate KV; the 48 DeltaNet linear-attention layers carry recurrent state and have no KV scales.

Visual QAlicense:apache-2.0
Context windowNot disclosed
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/11/2 19:16:42
OVERVIEW

Model overview

The scales were measured on the quantized model itself, not on the BF16 base, because activation-aware smoothing and int4 rounding change the K/V activations the served model actually writes to cache. Calibration replayed the exact same sequences that were used to calibrate the AWQ model: **112 packed sequences × 262,144 tokens (~29.4M tokens)** from the same Nemotron Post-Training v3 SFT blend (instruction-following chat, math, science, agentic code, multilingual), same shuffle seed, rendered with thinking enabled, so the measured KV distribution matches the deployment regime the weights were tuned for, at the exact context length the model serves at. While the model processed those sequences, we tracked the running maximum absolute value of the K states (after QK-norm) and V states each of the 16 global attention layers writes to the cache. Each scale is `amax / 448` (the fp8-e4m3 representable maximum), symmetric, per tensor, so no cached value can clip. The result is 32 per-layer scalars (`k_scale` / `v_scale`). Only the 16 global attention layers accumulate KV; the 48 DeltaNet linear-attention layers carry recurrent state and have no KV scales.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。