特定变体:量化方案的类型,采用了不同的量化方案来处理 attention.wv、attention.wo 和 feed_forward.w2 张量,量化方案见上表 q2_k: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors q3_k_l: Uses Q5_K for the attention.wv, attention.wo, and feed_forwa...
FP32、TF32、FP16、BF16、FP8、FP4、NF4、INT8都有什么关联,一文讲清楚 大模型的训练和推理,经常涉及到精度的概念,种类很多,而且同等精度级别下,还分不同格式,网上没看到一篇能够介绍全面的,这里梳理总结一份全面的介绍。 整体介绍 浮点数精度:双精度(FP64)、单精度(FP32、TF32)、半精度(FP16、BF16)、8...
return filetypeQ4_0, nil case "Q4_1": return filetypeQ4_1, nil case "Q4_1_F16": return filetypeQ4_1_F16, nil case "Q8_0": return filetypeQ8_0, nil case "Q5_0": return filetypeQ5_0, nil case "Q5_1": return filetypeQ5_1, nil case "Q2_K": return filetypeQ2_K, nil case...
Q8: How do you make our business long-term and good relationship? A:1. We keep good quality and competitive price to ensure our customer`s benefit. 2. We respect every customer as our friend sincerely and hope to do long-term business and make ...
Q4_0 17.507 76 1.53 Q4_1 17.187 72 1.68 Q5_0 16.194 78 1.60 Q5_1 15.851 81 1.68 Q8_0 15.652 89 2.13 FP16 15.623 117 2.82 FP32 15.623 198 5.64 With cuBLAS Measurements were made on Intel i7 13700K & NVIDIA 3060 Ti 8 GB. The model is RWKV-4-Pile-169M, 12 layers were offloa...
671b-q4_K_M:20小时前更新,大小404GB,哈希值 5da0e2d4a9e0 671b-q8_0:20小时前更新,大小713GB,哈希值 96061c74c1a5 fp16版本高达1.3TB显存占用,真土豪#DeepSeek-V3 编辑于 2025-01-17 15:20・IP 属地广东 写下你的评论... 登录知乎,您可以享受以下权益: ...
Q8: How do you make our business long-term and good relationship? A:1. We keep good quality and competitive price to ensure our customer`s benefit. 2. We respect every customer as our friend sincerely and hope to do long-term business and make friends with yo...
基于不同显卡的DeepSeek速度实测 总结:一般模型文件的大小比显存小,或者超过≤1G就可以跑的比较流畅,适用于所有本地大模型 😊。 集显: 建议:15b_Q4/15b_Q8 速度在15 tokens/s 🟢 可尝试:15b_f - 科技糖于20250227发布在抖音,已经收获了1.3万个喜欢,来抖音,
WeightType.Q4_0, WeightType.Q4_1, WeightType.Q5_0, WeightType.Q5_1, WeightType.Q8_0, WeightType.Q8_1, } KQUANT_TYPES = { WeightType.Q2_K, WeightType.Q3_K, WeightType.Q4_K, WeightType.Q5_K, WeightType.Q6_K, } IMATRIX_QUANT_TYPES = { ...
LLaMA v2 7B mostly Q8_0 6.67 GiB 6.74 B CUDA 999 1 pp 512 2041.33 ± 0.32 LLaMA v2 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 999 1 pp 512 2084.74 ± 0.08 LLaMA v2 7B mostly Q4_1 3.95 GiB 6.74 B CUDA 999 1 pp 512 2015.38 ± 0.69 LLaMA v2 7B mostly Q5_0 4.33 GiB 6.74 B CUDA...