q3_k_s: Uses Q3_K for all tensors q4_0: Original quant method, 4-bit q4_1: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models q4_k_m: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K q4_k_s: ...
filetypeQ5_K_M filetypeQ6_K filetypeIQ2_XXS filetypeIQ2_XS filetypeQ2_K_S filetypeQ3_K_XS filetypeIQ3_XXS filetypeUnknown ) func ParseFileType(s string) (filetype, error) { switch s { case "F32": return filetypeF32, nil case "F16": return filetypeF16, nil case "Q4_0": return fil...
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q6_K, GGML_TYPE_F32, 16, i, 256, { 1, 1}, {1, 1})); test_cases.emplace_back(new test_mul_mat(GGML_TYPE_IQ4_NL, GGML_TYPE_F32, 16, i, 256, { 1, 1}, {1, 1})); } */ #if 1 for (ggml_type type_a : ...
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llama_model_loader: - type q6_K: 1 tensors llm_load_vocab: special tokens cache size = 293 llm_load_vocab: token to piece cache size = 0,9338 MB llm_load_print_meta: format = GGUF V3 (latest) llm_load_print_meta: arch = qwen2moe ...
const uint8_t * restrict q6 = x[i].ql; const uint8_t * restrict qh = x[i].qh; @@ -8704,7 +8704,7 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r for (int i = 0; i < nb; ++i) { const float d_all = (float)x[i]...
WeightType.Q2_K, WeightType.Q3_K, WeightType.Q4_K, WeightType.Q5_K, WeightType.Q6_K, } IMATRIX_QUANT_TYPES = { WeightType.IQ1_M, WeightType.IQ1_S, WeightType.IQ2_XXS, WeightType.IQ2_XS, WeightType.IQ2_S, WeightType.IQ3_XXS, ...