Some more fine tuning
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2 changed files with 3 additions and 3 deletions
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@ -811,7 +811,7 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict
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float max_scale = 0; // as we are deducting the min, scales are always positive
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float max_min = 0;
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for (int j = 0; j < QK_K/32; ++j) {
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scales[j] = make_qkx1_quants(32, 31, x + 32*j, L + 32*j, &mins[j], 5, 0.f);
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scales[j] = make_qkx1_quants(32, 31, x + 32*j, L + 32*j, &mins[j], 9, 0.5f);
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float scale = scales[j];
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if (scale > max_scale) {
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max_scale = scale;
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@ -3726,7 +3726,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
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else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
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use_more_bits(i_attention_wv, n_attention_wv)) new_type = GGML_TYPE_Q6_K;
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else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
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else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S && i_attention_wv < 2) new_type = GGML_TYPE_Q5_K;
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else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S && i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
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else if (QK_K == 64 && (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) &&
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(i_attention_wv < n_attention_wv/8 || i_attention_wv >= 7*n_attention_wv/8)) new_type = GGML_TYPE_Q6_K;
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++i_attention_wv;
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@ -3740,7 +3740,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
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else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
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use_more_bits(i_feed_forward_w2, n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
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else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && i_feed_forward_w2 < 4) new_type = GGML_TYPE_Q5_K;
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else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S && i_feed_forward_w2 < 2) new_type = GGML_TYPE_Q5_K;
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else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S && i_feed_forward_w2 < 4) new_type = GGML_TYPE_Q5_K;
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++i_feed_forward_w2;
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} else if (name.find("attn_output.weight") != std::string::npos) {
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if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
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