Thomas 0451f10a42 Add GitHub data: filename sanitization (#640)

2025-07-23 13:31:53 +02:00

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1 - Offload Bitnet token embeddings to the GPU.md

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2 - Offload Bitnet token embeddings to the GPU - the right way.md

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3 - Merge mainline llama.cpp.md

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4 - Simdify and multi-thread tanh.md

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5 - Fusing a mat mul op followed by a scale op on the CPU.md

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6 - IQ4_K_ SOTA 4-bit quantization.md

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7 - Adding IQ2_K_ IQ3_K and IQ5_K.md

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9 - Fused soft cap and SIMD-ified GeLU.md

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10 - iq4_k_ speedup quantization by a factor of _2.md

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11 - Faster iq3_k and iq5_k quantization.md

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12 - q2_K_ allow it to detect ternary nets and quantize accordingly.md

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13 - Adding IQ2_TN for use with ternary models.md

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14 - Adding IQ6_K.md

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16 - Fix Makefile.md

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17 - Merge mainline - Aug 12 2024.md

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19 - Skip barriers of noops.md

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20 - iq2_k_ slightly better bpw - accuracy compromise.md

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21 - quantize_stats_ print rmse and max error as fraction of _x_.md

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22 - AVX2 quantization for Q8_K.md

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23 - iq4_k tweak.md

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24 - softcap_ minor improvement.md

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27 - Faster Gemma2.md

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28 - Binary KQ mask.md

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31 - Fix build when iqk_mul_mat is disabled.md

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32 - Zen4 Flash Attention.md

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33 - Do not process prompts containing binary data for escapes.md

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35 - Fix Zen4 Flash Attention.md

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36 - Zen4 Flash Attnetion 2.md

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37 - Performance improvements for legacy quants on ARM_NEON.md

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38 - Zen4 Flash Attention - bf16 support.md

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39 - Add support for bf16 to iqk_mul_mat.md

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40 - Adding bf16 support to CUDA.md

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41 - iqk_mul_mat_ARM_NEON_ adding bf16 support.md

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42 - Adding fused rms_norm.md

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43 - iq2_tn_ slightly faster PP on Zen4.md

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44 - Adding IQ1_TN - 1.6875 bpw for TriLM ternary models.md

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45 - Add CUDA support for IQ1_TN.md

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46 - IQ1_TN Metal implementation.md

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47 - iq2_tn_ slightly better performance on AVX2.md

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48 - AVX2 Flash Attention.md

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49 - ARM_NEON Flash Attention.md

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50 - AVX2 Flash Attention 2.md

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51 - Quantized Flash Attention for all supported CPU platforms.md

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52 - Fix bug and D _ 128 case for Q8_0 k-cache.md

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53 - Quantization mixes tweaks.md

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54 - Improve Q4_0 and Q8_0 performance on AVX2_Zen4.md

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55 - Improve Q5_0 performance on AVX2.md

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56 - BF16 support on Metal.md

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57 - AVX2_Zen4 horizontal sums.md

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58 - Fix compiler warnings.md

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61 - Adding ability to have meta data per tensor row.md

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62 - Use fp32 for K_Q in Metal FA implementation.md

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64 - Better sub-3-bit quantization mixes with a qkv tensor.md

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65 - Adding SWIGLU unary op.md

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66 - CUDA non-contiguous RoPE.md

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68 - It is time to fix replace_all.md

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69 - Allow bf16 kv-cache.md

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70 - Fused unary_x_y.md

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71 - iqk_mul_mat_ better srategy when nrc_y not divisible by ny.md

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72 - iqk_mul_mat_ better iq4_nl implementation on Zen4_AVX2.md

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73 - CUDA_ faster float -_ iq4_nl conversion.md

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74 - IQ4_NL kv-cache on the CPU _Zen4_AVX2_ARM_NEON_.md

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75 - Fix Q5_0 flash attention.md

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76 - iq4_nl_ faster quantization.md

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77 - Adding Q6_0.md

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78 - q6_0_ Slightly faster Zen4_AVX2.md

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79 - Do not quantize activations if not necessary.md

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80 - Move to c_17 projectwide.md

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81 - Cleanup scale fudge factors.md

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83 - New SOTA quantization_ 4.25 bpw IQ4_KS.md

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84 - Better model info.md

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85 - IQ2_KS_ 2.1875 bpw non-linear quantization.md

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86 - Fix and optimize iq2k Metal implementation.md

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87 - iq3_k_ fix and optimize Metal dot product.md

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89 - Adding IQ4_KSS_ 4.0 bpw quants.md

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90 - iq4_ks_ faster dot product on Metal.md

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91 - CLI - Specify GGML_TYPE to quantize for the main tensors..md

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93 - Attempt to blindly fix Windows build failure.md

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94 - Adding _agray3_s graph caching approach.md

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96 - Quant strategies_ attn_q Q4 _ attn_v Q6 for Llama 3.1 Q5_K_S.md

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97 - Bitnet_ make the scale tensors optional.md

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98 - Avoid rebuild of GGML graph for each token.md

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99 - Enable IQ4_NL for KV-cache in token generation using Flash Attention.md

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101 - Enable q6_0 in flash attention.md

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102 - Add support for Granite and GraniteMoE models.md

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105 - Fix quantized k-cache without FA.md

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106 - Bitnet changes.md

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107 - Faster IQ1_BN Metal implementation.md

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108 - Another Bitnet performance improvement on Metal.md

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109 - Bitnet CUDA improvements.md

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110 - Bitnet_ use the fused mul-silu in the FFN network.md

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111 - Use fused mul - unary op also for MoE models.md

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112 - Faster MoE inference.md

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113 - Trellis quantization.md

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114 - MMQ Kernel for Q6_0 _pretty please_.md

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115 - MMQ for Q6_0.md

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116 - Use Q6_0 instead of Q5_1 for tensors incompatible with IQ5_K_Q5_K.md

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117 - Some minor quant strategies tweaks.md

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118 - IQ4_NL_X4.md

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119 - Q4_0_R4.md

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120 - Q8_0_R4.md

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121 - Q5_0_R4.md

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122 - Q6_0_R4.md

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123 - IQ4_XS_R4.md

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124 - iq2_bn_r4_ fastest Bitnet CPU implementation on the planet.md

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125 - R4 improvements on ARM_NEON.md

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126 - Rename iq4_nl_x4 to iq4_nl_r4.md

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127 - Q4_0_R4 on CUDA.md

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128 - Faster IQ4_XS_R4 on Zen4.md

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129 - Q4_K_R4.md

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130 - Q6_K_R4.md

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131 - Slightly faster Q4_K_R4 and IQ4_XS_R4 on Zen4.md

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132 - Q5_K_R4.md

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134 - Q3_K_R4.md

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135 - Better ARM_NEON implementation for R4 quants.md

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136 - Q2_K_R4.md

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137 - Fix AVX2 implementation of iq4_nl_r4.md

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138 - IQ4_K_R4.md

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139 - Faster R4 quants on Zen4.md

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141 - Q8_K_R8_ Fastest quantized matrix multiplications.md

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142 - BF16_R16 - 16 interleaved bf16 rows.md

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143 - Slightly faster IQ4_XS_R4 on AVX2.md

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144 - Slightly faster IQ4_K_R4 on AVX2_Zen4.md

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145 - IQ3_K_R4.md

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146 - IQ2_K_R4.md

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147 - Be able to repack tensors at run time.md

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148 - Slightly better matrix x vector on Zen4_AVX2 for iq2_k_r4_ iq3_k_r4_ iq.md

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149 - IQ5_K_R4.md

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150 - IQ4_KS_R4.md

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151 - fix typo.md

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152 - IQ3_XXS_R4.md

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153 - IQ3_XXS_R4.md

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154 - IQ2_XXS_R4.md

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155 - IQ2_XS_R4.md

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156 - IQ2_S_R4.md

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157 - R4 i-quants improvements.md

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158 - Faster R4 legacy quants.md

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161 - MSVC fixes.md

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162 - IQ3_S_R4.md

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163 - q4_0_r4_ Use AVX2 version for matrix x vector.md

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168 - Falcon3 changes.md

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169 - Be able to re-quantize MS BitNet I2_S models.md

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170 - MoE fix for R4 quants.md

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171 - Fix lower FA performance for even batch sizes.md

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172 - CPU Flash Attention improvements.md

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173 - More Flash Attention improvements.md

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174 - On Zen4 repack fp16 models to bf16_r16.md

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175 - Better BF16 support on AVX2.md

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176 - Deepseek V3 support added.md

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177 - Update chat templates.md

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178 - Interleave 8 rows _Q8_0_ IQ4_XS_.md

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179 - Minor performance improvements.md

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180 - Deepseek MLA Optimizations.md

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181 - Various.md

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182 - Faster Q4_K_R4 and Q5_K_R4 on AVX2_Zen4.md

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184 - Deepseek-Lite.md

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185 - IQ1_S_R4_ better 1.5 bpw quants.md

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186 - iq1_s_r4_ slightly faster NEON gemm_gemv.md

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187 - IQ1_M_R4_ better 1.75 bpw quants.md

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188 - Add optional MLA.md

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189 - Rename q4_0_r4_ q8_0_r4 and iq4_xs_r4 to _r8.md

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190 - cuda_ non-contiguous rms norm.md

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191 - Add additional checks for iq1_s_r4 quantization.md

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192 - Revert _79.md

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193 - RPC sync.md

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194 - Use Q8_K_128 for IQ1_S_R4 and IQ1_M_R4 matrix multiplications.md

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195 - Deepseek MLA Optimizations V2.md

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197 - FA_ Add option to build all FA kernels.md

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198 - Load all MoE experts during warmup and make warmup 1 token.md

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200 - DeepSeek FA support _CPU only_.md

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202 - Fix imatrix overprotectiveness.md

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204 - Fix iqk_mul_mat on AVX512 systems that are missing BF16 support.md

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205 - Faster MLA prompt processing.md

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206 - MLA_ allow Q8_0 K-cache for MLA.md

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207 - Faster CPU TG for GQA models.md

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208 - Q8_KV_ 8-bit quantization type targeting the KV cache.md

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210 - Repack also experts.md

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212 - Optimized GEMM_GEMV for IQ1_S.md

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213 - Fix NEON gemm_gemv for legacy quants when row size is not divisible by .md

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215 - Trying to fix confusion betweem HAVE_FANCY_SIMD and AVX512.md

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216 - Hopefully this really fixes the confusion between AVX512 and FANCY_SIMD.md

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218 - Better strategy for attention matrix multiplications when generating to.md

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219 - Fuse MoE up and gate matrix multiplications.md

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220 - Fix _217.md

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225 - Examples _ Add new sweep-bench benchmark.md

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226 - Fix compilation error with IQK_FA_ALL_QUANTS enabled.md

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229 - Fused MoE ffn_up and ffn_gate.md

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231 - Fix _230.md

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232 - Give the user the option to override where model weights are stored.md

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233 - Slightly faster CUDA MLA.md

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234 - Faster MLA on CUDA.md

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235 - Option to use MLA without a transposed cache.md

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236 - Feat_lock free server.md

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237 - Reduce size of compute buffers.md

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238 - A better way to measure the cost of ggml_barrier.md

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239 - SER - Smart Expert Reduction.md

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240 - Flash MLA _CPU only_.md

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241 - DeepSeek CUDA Flash Attention.md

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243 - Better FlashMLA.md

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244 - Custom quantization rules with regular expressions.md

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246 - Faster FlashMLA prompt processing.md

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247 - FlashMLA on CUDA.md

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248 - Faster MoE token generation on CUDA.md

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250 - DeepSeek imatrix stuff.md

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251 - Try using fp32 for FlashMLA.md

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252 - MLA-2_ Allow usage of q8_0 for KV cache on CUDA.md

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253 - FlashMLA-2 _CPU_ faster and smaller compute buffer size.md

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259 - Prepare wk_b tensors of DeepSeek models on the fly.md

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260 - FlashMLA-2_ reduce compute buffer size _CUDA and CPU_.md

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261 - Compile time option to use bf16 for quants without MMQ kernels.md

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262 - Fix _261.md

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264 - Make Q8_0 KV cache work with FlasMLA-2 on CUDA.md

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265 - Allow q8_0 cache on the CPU for FlashMLA-2.md

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268 - Prevent FlashMLA-1 from running on CUDA.md

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269 - Fix ggml_compute_forward_dup_q.md

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270 - Honor mmap setting when using tensor overrides.md

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272 - Convert models to row-interleaved quants using the quantize tool.md

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273 - FlashMLA-3_ the best of both worlds _CPU only_.md

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274 - Specify tensor name regex for tensors to be repacked.md

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275 - Fix bug_ missing parentheses in logical expression.md

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276 - Add Gemma3 support _text only_.md

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277 - Attempt to improve FlashMLA on the CPU.md

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278 - Test transparent huge pages on Linux.md

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279 - Fighting with cmake.md

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280 - Native build ooption for CUDA when GGML_NATIVE is set.md

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282 - Improve DeepSeek batched processing speed.md

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283 - CUDA_ better MoE implementation.md

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284 - llama-bench_ enable having different number of threads for tg and pp.md

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287 - Is this better for DeepSeek-R1_.md

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289 - Update sweep bench _depracating .jsonl support_.md

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290 - mmap backed KV cache.md

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291 - Disable Zen4 optimizations for Q8_0_Q8_0_R8.md

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292 - Use bf16 instead of fp16 block scales for q8_1.md

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294 - Make sure tensor row size is multiple of block size also when quantizin.md

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295 - Quantization improvements.md

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298 - Update gguf-py constants.md

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299 - Additional guards for interleaved quants.md

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301 - Fix _300.md

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302 - Quantization improvements _2_.md

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303 - Fix ARM_NEON build failure due to q8_2.md

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307 - Metal_ much faster MoE prompt processing.md

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309 - Fix GCC compilation errors on ARM.md

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310 - Metal_ FA and FlashMLA.md

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311 - Add -flax-vector-conversions for GCC on ARM.md

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312 - Improved IQ2_XS quantization.md

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313 - We need to synchronize before using device to host async memcpy.md

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315 - Try not repacking q8_0 for FA computations.md

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317 - Add copyright notices.md

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318 - Use links for ggml_llama.cpp authors.md

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320 - Guard against attempts to use MLA for non-MLA models.md

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321 - LlaMA-4 support _text only_.md

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324 - Correct L4 rms_norm.md

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325 - Fix KLD precision.md

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326 - WIP Compute per layer LIM Scores during imatrix.md

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327 - Improved IQ1_M quantization.md

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328 - imatrix_ collect layer influence statistics.md

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329 - Add ability to hide imatrix details in llama-quantize.md

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330 - Allow q8_0 KV cache for head size 256.md

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331 - Better gemm_gemv on AVX2 fr q4_0_r8.md

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332 - Better TG performance for GQA models _CPU_.md

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333 - Support GLM-4-0414 models based on piDack_s mainline PR.md

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336 - Fix termux_android build.md

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337 - Add support for bitnet2b_2501 model.md

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338 - BitNet adjustments.md

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341 - Add support for Cohere2.md

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342 - Fix LLaMA-4 attention.md

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343 - cuda_ use switch in constexpr funcs.md

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344 - Add GLM-4-0414 Model Support.md

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346 - Fix FA on ARM CPUs.md

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347 - Add ability to manually set arch flags.md

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348 - Fix q4_1 and q5_1 on Arm.md

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349 - Fix division by zero bug.md

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351 - CPU FA improvements.md

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352 - Update README.md.md

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355 - Apply Qwen3 PR from llama.cpp.md

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356 - Add missing enum values for qwen3 and qwen3moe.md

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360 - Fix IQK_FA_ALL_QUANTS on AVX2.md

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364 - Fix FA bug on AVX2.md

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366 - Add support for new Bitnet model architecture name.md

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368 - Trying to fix iq1_s_r4_iq1_m_r4 quantization failure.md

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369 - cmake_ force MSVC compiler charset to utf-8.md

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370 - CUDA_ faster FA TG for GQA models.md

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371 - Another attempt to fix _367.md

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374 - CUDA_ MMQ for IQ4_KS.md

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375 - Add batch warmup to sweep-bench.md

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377 - Support for Llama-3-Nemotron models.md

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382 - Fix DeepSeek FA.md

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386 - FlashMLA-3 for DeepSeek models on CUDA.md

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390 - Fix build for Xeon Gold 6226R.md

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391 - Fix DeepSeek q8_0 cache.md

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392 - fix some MSVC build problem..md

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394 - Handle incompatible DeepSeek GGUFs.md

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400 - Fix CUDA DeepSeek FlashMLA-3 with quantized KV cache.md

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402 - Fix missing rope_freqs with convert_hf_to_gguf.md

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404 - TG improvements for MoE models.md

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405 - GPU offload policy.md

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406 - Fix race in the CUDA DeepSeek FA kernel.md

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408 - Faster DeepSeek FA on CUDA.md

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409 - Enable faster prompt processing with mainline llama.cpp GGUFs.md

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410 - Better CPU FA performance for DeepSeek-Lite.md

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411 - Fix imatrix calculation for MLA models.md

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413 - Fix new CUDA FA on Touring.md

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415 - Fix SER _CPU_.md

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416 - Fix SER _CUDA_.md

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417 - CUDA_ quantized GEMM for for IQ4_K_ IQ5_K_ IQ6_K.md

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418 - CUDA_ quantized GEMM for for IQ2_KS_ IQ2_K_ IQ3_K.md

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421 - Fix standard attention on the CPU.md

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422 - Adding IQ5_KS - 5.25 bpw quants.md

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424 - Adding forgotten template instance for iq5_ks.md

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426 - IQ5_KS_R4_ row-interleaved IQ5_KS.md

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427 - Fix AVX2 implementation of IQ4_K_ IQ4_KS_ IQ5_K_ IQ6_K.md

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428 - Zen4_ Faster PP for IQ2_KS_ IQ4_KS_ IQ5_KS.md

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429 - Option to enable or disable the CPU FA kernels.md

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430 - Disable multi-add for now.md

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431 - Forgotten MMQ ref and typo.md

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435 - Refactor iqk_mul_mat.cpp.md

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438 - Another attempt to fix the illegal memory access bug.md

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439 - Bug fixes from mainline.md

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441 - Trellis quants with CPU inference.md

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442 - CUDA call tracer.md

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443 - Streamline a bit the quant strategies.md

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444 - gguf-split _ update.md

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445 - Fix typo in non-AVX2 code branch.md

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446 - Fix bug in MMVQ kernel.md

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448 - Fix MSVC compilation.md

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449 - Legacy quants conversion schemes in convert_hf_to_gguf.py.md

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453 - Faster IQ3_KT and IQ4_KT.md

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454 - Add support for FP8 GGUF creation and re-quantization _WIP_.md

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457 - Remove GGML_IQK_MUL_MAT option.md

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458 - Add missing gguf-py constants.md

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460 - aarch64 kernels for KT quants.md

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461 - CUDA implementation for IQ2_K_R4_ IQ3_K_R4_ IQ4_K_R4_ IQ5_K_R4.md

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462 - CUDA GEMM and GEMV for IQ4_KS_R4 and IQ5_KS_R4.md

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465 - Set cache_prompt default to true.md

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468 - Minor _2_ iq2_ks TG performance improvement on CUDA.md

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469 - Replace MLA-specific KV cache with the standard KV cache.md

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470 - Send _DONE_ for OAI compatibility.md

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471 - NEON implementation for trellis quants.md

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473 - Replace MLA-specific KV cache with the standard KV cache V2.md

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475 - Metal implementatio for the trellis quants..md

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478 - forgotten refs and typo.md

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480 - Rpc improvement.md

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481 - Webui improvement.md

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482 - Trellis quants_ faster CPU prompt processing.md

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483 - convert_hf_to_gguf.py _ conversion from hf weights to Q6_0.md

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484 - BF16 Trellis implementation.md

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486 - Adding the XTC sampler.md

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487 - Make sure MMVQ is supported before using it.md

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488 - Faster CPU prompt processing for Trellis quants and MoE models.md

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489 - Adding top-n-sigma sampler.md

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492 - CUDA implementation for IQ1_S_R4.md

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493 - MMQ implementation for IQ4_KS_R4 and IQ5_KS_R4.md

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494 - IQ1_M_R4 CUDA implementation.md

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495 - Check if ffn_up and ffn_gate are of the same type before using fmoe.md

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496 - Quick hack_ add the MLA flag to llama_hparams.md

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497 - Make prompt cache saving and restoring MLA aware.md

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501 - Fix _499.md

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502 - Add an endpoint that lists all the saved prompt caches to server.md

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504 - Add DRY and fix the server to use other new samplers..md

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505 - New IQ4_KT trellis implementation.md

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506 - Fix non rpc build error.md

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508 - Fix Compile error _C2668_.md

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509 - Docs update.md

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510 - Update News section of readme.md

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511 - New IQ2_KT.md

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512 - Add top n sigma sampler in webui and other webui fix.md

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513 - add dry sampler.md

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515 - IQ2_XXS_ much faster CPU prompt processing.md

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516 - Much faster iq3_xxs GEMM via repacking to q8_0_r8 _AVX2_.md

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517 - IQ1_S_ much faster CPU prompt processing.md

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518 - IQ3_S_ much faster CPU prompt processing.md

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520 - Better strategy for GPU offload.md

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524 - Perhaps a slightly better GEMV version for IQ2_XXS_ IQ3_XXS_ IQ3_S.md

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525 - Faster CPU prompt processing for Q4_K and Q5_K.md

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528 - Fix bug introduced in _524_525.md

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529 - New IQ2_KT_ IQ3_KT and IQ4_KT_ V2.md

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531 - Much faster CPU prompt processing _part 1_.md

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533 - Much faster CPU prompt processing _part 2_.md

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534 - Much faster CPU prompt processing _part 3_.md

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535 - Minor readme update.md

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536 - Fix KT Neon _ ARM typo.md

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537 - Update CMakeLists.txt to fix NDEBUG handling.md

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540 - Fix missed block_q8_x2 bf16 -_ i16 change.md

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541 - Perhaps slightly faster trellis quants.md

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542 - Fix NEON build.md

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544 - New integer trellis on ARM_NEON.md

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546 - Faster ARM_NEON GEMM implementation for legacy quants.md

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547 - build_ add script to simplify build_test workflow for Android.md

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549 - Much faster prompt processing for IQK quants _ARM_NEON_.md

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550 - Much faster prompt processing for I-quants _ARM_NEON_.md

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552 - Much faster prompt processing for k-quants _ARM_NEON_.md

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553 - Much faster prompt processing for IQ1_S and IQ1_M on ARM_NEON.md

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554 - Update README.md to add quickstart section.md

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555 - Add Falcon-Edge support.md

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557 - CUDA_ MMQ for iqX_r4 quants.md

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558 - Add mikupad to ik_llama as an alternative WebUI.md

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559 - Use cuBLAS for large batches and quants with block size 16.md

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560 - Remove what appears to be unnecessary asserts in ggml_cuda_cpy.md

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563 - Merge vulkan code from mainline up to commit of 6_28_2025.md

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565 - add hunyuan moe support for 561.md

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566 - Adding IQ3_KS quants.md

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567 - Minor CUDA PP speed improvement.md

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569 - Conditionally disable fused ops when building with Vulkan enabled.md

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570 - Remove duplicate_misplaced cmake find_package for Vulkan.md

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571 - Fix CMakeLists.md

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573 - Support for dots.llm1 models.md

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574 - Change KQ mask padding to 64.md

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577 - Vulkan_ fused rms norm.md

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578 - Do not crash when there is no DRY sampler.md

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579 - Fix debug build failure with RPC off.md

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580 - Vulkan_ add GGML_OP_FUSED_MUL_UNARY.md

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581 - Vulkan_ Disable multi-add for now.md

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582 - Vulkan_ adding GGML_OP_MULTI_ADD implementation.md

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583 - Adding forgotten file.md

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584 - Vulkan_ flash attention for DeepSeek models.md

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585 - Special handling of Seed Coder FIM tokens.md

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587 - Fix crash when there is no DRY sampler.md

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588 - Fix server crash when there is no DRY sampler.md

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589 - CUDA_ small PP performance improvement for MoE models.md

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592 - Another minor readme update.md

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593 - Faster prompt processing for IQ2_KS_ IQ2_K_ IQ2_K_R4.md

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595 - CUDA_ Faster prompt processing for several quantization types.md

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598 - Vulkan_ iquants and flash attention split_k_reduce improvement.md

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602 - Adding IQ2_KL.md

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603 - Check if MMQ should be used before using it.md

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604 - Fix attn_v conditionality when quantizing..md

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606 - Add iq3_ks to constants.py.md

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607 - vulkan_ support softmax_FA batch and broadcast.md

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608 - Vulkan_ a fresh start.md

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609 - Added kimi-k2 support _ported from llama.cpp_.md

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610 - q8_k_r8_ experimental AVX512 version.md

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611 - Bump GGML_MAX_CONTEXTS to allow loading more shards.md

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612 - kimi-k2 convert script and chat template.md

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616 - Adding IQ1_KT - 1.75 bpw SOTA quants.md

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617 - Fixup kimi-k2 convert indentation.md

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618 - Webui_ New Features for Conversations_ Settings_ and Chat Messages.md

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620 - Bump Windows max open files from 512 to 2048.md

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622 - Add GGML_MAX_CONTEXTS definition in CMakeLists.txt.md

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624 - Quantization tweaks.md

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628 - _Draft_ Function calling support for Kimi-K2.md

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630 - GEMM for IQ1_M.md

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