Update README.md

Author: ikawrakow

README.md

@@ -10,15 +10,15 @@ This repository is a fork of [llama.cpp](https://github.com/ggerganov/llama.cpp)
 
 ### Model Support
 
-LlaMA-3-Nemotron [PR 377](https://github.com/ikawrakow/ik_llama.cpp/pull/377), Qwen3 [PR 355](https://github.com/ikawrakow/ik_llama.cpp/pull/355), GLM-4 [PR 344](https://github.com/ikawrakow/ik_llama.cpp/pull/344), Command-A [PR 341](https://github.com/ikawrakow/ik_llama.cpp/pull/341), bitnet-b1.58-2B-4T [PR 337](https://github.com/ikawrakow/ik_llama.cpp/pull/337), LLaMA-4 [PR 321](https://github.com/ikawrakow/ik_llama.cpp/pull/321), Gemma3 [PR 276](https://github.com/ikawrakow/ik_llama.cpp/pull/276),  DeepSeek-V3 [PR 176](https://github.com/ikawrakow/ik_llama.cpp/pull/176)
+LlaMA-3-Nemotron [PR 377](https://github.com/ikawrakow/ik_llama.cpp/pull/377), Qwen3 [PR 355](https://github.com/ikawrakow/ik_llama.cpp/pull/355), GLM-4 [PR 344](https://github.com/ikawrakow/ik_llama.cpp/pull/344), Command-A [PR 341](https://github.com/ikawrakow/ik_llama.cpp/pull/341), bitnet-b1.58-2B-4T [PR 337](https://github.com/ikawrakow/ik_llama.cpp/pull/337), LLaMA-4 [PR 321](https://github.com/ikawrakow/ik_llama.cpp/pull/321), Gemma3 [PR 276](https://github.com/ikawrakow/ik_llama.cpp/pull/276),  DeepSeek-V3 [PR 176](https://github.com/ikawrakow/ik_llama.cpp/pull/176), Kimi-2 [PR 609](https://github.com/ikawrakow/ik_llama.cpp/pull/609), dots.llm1 [PR 573](https://github.com/ikawrakow/ik_llama.cpp/pull/573), Hunyuan [PR 565](https://github.com/ikawrakow/ik_llama.cpp/pull/565)
 
 ### Quantization
 
 #### Quantization additions
 
-##### Trellis quants (`IQ2_KT`, `IQ3_KT`, `IQ4_KT`)
+##### Trellis quants (`IQ1_KT`, `IQ2_KT`, `IQ3_KT`, `IQ4_KT`)
 
-Information and the original CUDA implementation in [PR 113](https://github.com/ikawrakow/ik_llama.cpp/pull/113). Additional implementations: Metal [PR 475](https://github.com/ikawrakow/ik_llama.cpp/pull/475), Neon [PR 471](https://github.com/ikawrakow/ik_llama.cpp/pull/471), CPU [PR 441](https://github.com/ikawrakow/ik_llama.cpp/pull/441)
+Information and the original CUDA implementation in [PR 113](https://github.com/ikawrakow/ik_llama.cpp/pull/113). Additional implementations: Metal [PR 475](https://github.com/ikawrakow/ik_llama.cpp/pull/475), Neon [PR 471](https://github.com/ikawrakow/ik_llama.cpp/pull/471), CPU [PR 441](https://github.com/ikawrakow/ik_llama.cpp/pull/441). `IQ1_KT` was added more recently in [PR 616](https://github.com/ikawrakow/ik_llama.cpp/pull/616). Note: these are base on a novel, integer-base trellis, which allows to achieve reasonable CPU performance, see [PR 529](https://github.com/ikawrakow/ik_llama.cpp/pull/529) and PRs quoted there for details. 
 
 ##### IQK quants
 
@@ -28,12 +28,16 @@ Initial implementations (Zen4, AVX2, NEON): `IQ5_KS_R4` [PR 426](https://github.
 
 Cuda implementations:  `IQ4_KS_R4` and `IQ5_KS_R4` [PR 493](https://github.com/ikawrakow/ik_llama.cpp/pull/493), `IQ1_S_R4` [PR 492](https://github.com/ikawrakow/ik_llama.cpp/pull/492), `IQ1_M_R4` [PR 494](https://github.com/ikawrakow/ik_llama.cpp/pull/494). `IQ4_KS_R4` and `IQ5_KS_R4` [PR 462](https://github.com/ikawrakow/ik_llama.cpp/pull/462), `IQ2_K_R4`, `IQ3_K_R4`, `IQ4_K_R4`, `IQ5_K_R4` [PR 461](https://github.com/ikawrakow/ik_llama.cpp/pull/461), `IQ4_K, IQ5_K, IQ6_K` [PR 417](https://github.com/ikawrakow/ik_llama.cpp/pull/417), `IQ2_KS, IQ2_K, IQ3_K` [PR 418](https://github.com/ikawrakow/ik_llama.cpp/pull/417)
 
+`IQ2_KL` is a more recent addition in [PR 602](https://github.com/ikawrakow/ik_llama.cpp/pull/602) 
+
 #### Quantization improvements
 
 `IQ1_M` [PR 327](https://github.com/ikawrakow/ik_llama.cpp/pull/327), `IQ2_XS` [PR 312](https://github.com/ikawrakow/ik_llama.cpp/pull/312), `Q2_K, Q4_K, Q5_K, Q4_1, Q5_1` [PR 302](https://github.com/ikawrakow/ik_llama.cpp/pull/302), `Q4_0, Q5_0, Q6_0, Q3_K, Q6_K, IQ4_XS, IQ4_NL` [PR 295](https://github.com/ikawrakow/ik_llama.cpp/pull/295)
 
 #### Quantization performance improvements 
 
+* Much faster CPU prompt processing for all non-interleaved quants. Initial idea in [PR 515](https://github.com/ikawrakow/ik_llama.cpp/pull/515) and [PR 531](https://github.com/ikawrakow/ik_llama.cpp/pull/531), with many follow up PRs to apply to all quantization types for the 3 supported CPU platforms.
+* All quantization types now have quantized matrix multiplication CUDA kernels, see [PR 557](https://github.com/ikawrakow/ik_llama.cpp/pull/515) and several others
 * Faster CPU prompt processing for Trellis quants and MoE models. [PR 488](https://github.com/ikawrakow/ik_llama.cpp/pull/488)
 * Trellis quants: faster CPU prompt processing [PR 482](https://github.com/ikawrakow/ik_llama.cpp/pull/482).
 * Minor (~2%) `iq2_ks` TG performance improvement on CUDA [PR 468](https://github.com/ikawrakow/ik_llama.cpp/pull/468)
@@ -43,6 +47,8 @@ Cuda implementations:  `IQ4_KS_R4` and `IQ5_KS_R4` [PR 493](https://github.com/i
 
 ### Features
 
+* Function call support [PR 628](https://github.com/ikawrakow/ik_llama.cpp/pull/628)
+* Webui: New Features for Conversations, Settings, and Chat Messages [PR 618](https://github.com/ikawrakow/ik_llama.cpp/pull/618)
 * Legacy quants conversion schemes in `convert_hf_to_gguf.py` [PR 449](https://github.com/ikawrakow/ik_llama.cpp/pull/449), `Q6_0` in [PR 483](https://github.com/ikawrakow/ik_llama.cpp/pull/483)
 * June 8 2025: Webui updated (legacy still available when `--path ./examples/server/public_legacy` is passed) [PR 481](https://github.com/ikawrakow/ik_llama.cpp/pull/481)
 * June 8 2025: RPC improvements [PR 480](https://github.com/ikawrakow/ik_llama.cpp/pull/480)
@@ -62,6 +68,7 @@ Cuda implementations:  `IQ4_KS_R4` and `IQ5_KS_R4` [PR 493](https://github.com/i
 
 ### Performance improvements
 
+* Better GPU offload strategy for MoE models when using hybrid HPU/CPU inference, see [PR 520](https://github.com/ikawrakow/ik_llama.cpp/pull/520) 
 * May 13 2025: Better CPU FA performance for DeepSeek-Lite. [PR 410](https://github.com/ikawrakow/ik_llama.cpp/pull/410)
 * May 11 2025: Slightly faster flash attention for DeepSeek models on CUDA, along with extending compatibility to Touring or newer GPUs. [PR 408](https://github.com/ikawrakow/ik_llama.cpp/pull/408)
 * May 4 2025: Significant token generation performance improvement on CUDA with Flash Attention for GQA models. For details and benchmarks. [PR 370](https://github.com/ikawrakow/ik_llama.cpp/pull/370)