量化模型在 CPU 上的速度明显快于 FP16 模型（大约 19 个 token/秒，而 9 个 token/秒）。小型和中型版本的性能大致相同。使用重要矩阵量化的模型表现并无不同。对于像 Gemma 2 2B 这样的小型 LLM，我认为使用中型版本（Q4_K_M）更好，因为它只比小型版本大 70 MB。结论 K 量化通过将权重量化为具有单独...
接下来,我们使用重要性矩阵和不重要性矩阵对模型进行量化,以进行比较,并使用两种不同的方法“Q4_K_S”和“Q4_K_M”。Q4_K_S 产生的模型比 Q4_K_M 略小,但准确性较低。 对于方法中的m : qtype = f“ {quantized_path} / {m.upper()} .gguf” iqtype = f“ {quantized_path}...
static void quantize_row_q4_0_reference(const float * restrict x, void * restrict y, int k) { assert(k % QK == 0); const int nb = k / QK; const size_t bs = sizeof(float) + QK/2; uint8_t * restrict pd = ((uint8_t *)y + 0*bs); uint8_t * restrict pb = ((uin...
| **Qwen2.5-Coder-32B-Instruct-GGUF-Q4_K_M** | 90.2 | 84.8 | 81.4 | 82.3 | 85.5 | 86.3 | 80.1 | 50.6 | 80.2 | | **Qwen2.5-Coder-32B-Instruct-GGUF-Q4_0** | 88.4 | 82.9 | 80.1 | 81.0 | 86.8 | 85.7 | 78.3 | 48.1 | 78.9 | | **Qwen2.5-Coder-32B-Instruct-GGUF-Q3...
value == "phi3-mini-instruct": model_id = "microsoft/Phi-3-mini-4k-instruct" model_path = "./phi3/" model_fp16 = "Phi-3-mini-4k-instruct.Fp16.gguf" model_gguf = "Phi-3-mini-4k-instruct.Q4_K_M.gguf" elif model.value == "llama-2-7b-chat": model_id = "meta-llama/...
JICl4YFDYfNbkbBh5JDgrazFml50xEQQwQUjxNwE0IDSofLzSg7UNVKn+Rr1KErzBHUxBqdHRlXzqYsIa5K9Y0UuE2ugw3g5KYofm7AaGNTzJSMhcchhxdaU4JZ0F1UNgQ8XcGDguypqYza8yFaEoGgNRcLej+g2t0feGKFE5OY2PFluQ3q4HgycxlfvzHqo0KcM0JI8OKXtzayJFgsqC1NdUQVu8rChnA6FO3MFyGOoC9KO8ITPpYM5pRqTlczFkLES/4u5Ip...
Here, M is a suf- ficiently large integer, and Souti ·Iouti 2 can approximate the result of Softmax(xi). 2Sout is the scaling factor for the kout-bit symmetric quantization with m ≈ 1. Algorithm 1: Integer-only Softmax: Shiftmax Input: Output: Iin ...
McKinnon, K. M. Flow cytometry: An overview.Current Protocols in Immunology120,https://doi.org/10.1002/cpim.40(2018). Maecker, H. T. & Trotter, J. Flow cytometry controls, instrument setup, and the determination of positivity.Cytometry Part A69A, 1037–1042.https://doi.org/10.1002/cyto...
"general.file_type":GGMLFileQuantizationType.MOSTLY_Q4_K_M, "general.name":"gemma-2b-it", "general.quantization_version":2, "gemma.attention.head_count":8, Expand DownExpand Up@@ -171,7 +173,7 @@ describe("gguf", () => { ...
Q3_K: AsQ5_K, but using 3 bits per quant, so3.5625bits per weight. Q4_K: AsQ5_K, but using 4 bits per quant, so4.5625bits per weight Here are some model sizes and perplexities where output.weightis always quantized withQ6_0