DeepSeek-V4.1-Flash-1017-EXL3-EAQ-v1-2.9-bpw

모델 소개

EAQ v1 (EXL3 format) quantization of deepseek-ai/DeepSeek-V4.1-Flash-1017 (revision `1bae0e10`, the refreshed V4.1-Flash weights), converted with exllamav3 `v1.6.1` plus the EAQ patch set (converter version 1.6.1) and a port of the DeepSeek-V4.1 architecture (see Conversion). Teacher-forced next-token fidelity to the original source weights was measured in a shared evaluation port; see **Quality vs the original model**. This is **not** a drop-in replacement for the original FP8/FP4 checkpoint. The Engram embedding tables are **not** in this repo: you still need shards 47 and 48 from the base model (about 95 GiB each). **Format: plain EXL3 (EAQ v1).** EAQ v1 currently uses the standard EXL3 format unchanged: `quant_method` is `exl3`, codebook `mul1`, `out_scales=always`, and the tensor keys and the main-model per-tensor bit widths are identical to the standard EXL3 2.9 bpw builds of this model (checked against LibertAIDAI/DeepSeek-V4.1-Flash-1017-EXL3-2.9bpw: 192,495 tensor keys; the widths of all 46,702 main-model quantized tensors match; tokenizer files byte-identical). The 46 MTP / DSpark dense tensors differ: 4 bit here, 6 bit in the LibertAI build. What EAQ v1 changes is only how the weights were produced (the EAQ conversion; see Conversion). Any runtime that loads EXL3 DeepSeek-V4.1 checkpoints should load this one without EAQ-specific code, including the TensorFold DeepSeek-V4.1-Flash-1017 2x DGX Spark recipe (set its `MODEL_ID` to this repo).

주요 특징

特点摘要待依据来源补齐，不凭模型名称推断能力。

한계 및 유의 사항

구체적 한계를 판단할 출처가 부족합니다. 실제 성능, 지연, 데이터 정책, 라이선스와 버전 수명을 확인하세요.

출처별 요금 및 비용 분석

요금은 채널, 지역, 설정에 따라 다릅니다. 각 채널 요금은 개별 표시되며 제공사의 통일 공식 요금이 아닙니다.

공개 평가 및 실행 설정

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。

자주 묻는 질문

출처 및 추적 기록

许可证：mit。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。