Update News section of readme (#510)

* Convert existing News to new format

* Update with new ones

* Add more links and minor fix

* more minor fixes

* requested changes

* Add old PRs

* Add more old PRs

* Add all IQK quants

1 files changed, 87 insertions, 39 deletions

diff --git a/README.md b/README.md
index 7c8902fd..c0c53505 100644
--- a/README.md
+++ b/README.md
@@ -8,48 +8,96 @@ This repository is a fork of [llama.cpp](https://github.com/ggerganov/llama.cpp)
 
 ## Latest News
 
+### 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)
+
+### Quantization
+
+#### Quantization additions
+
+##### Trellis quants (`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)
+
+##### IQK quants
+
+Information can be found in [Discussion 8](https://github.com/ikawrakow/ik_llama.cpp/discussions/8).
+
+`IQ5_KS_R4` [PR 426](https://github.com/ikawrakow/ik_llama.cpp/pull/426), `IQ5_KS` [PR 422](https://github.com/ikawrakow/ik_llama.cpp/pull/422), `IQ4_KS_R4` [PR 150](https://github.com/ikawrakow/ik_llama.cpp/pull/150), `IQ5_K_R4` [PR 149](https://github.com/ikawrakow/ik_llama.cpp/pull/149), `IQ2_K_R4` [PR 146](https://github.com/ikawrakow/ik_llama.cpp/pull/146), `IQ3_K_R4` [PR 145](https://github.com/ikawrakow/ik_llama.cpp/pull/145), `IQ4_K_R4` [PR 138](https://github.com/ikawrakow/ik_llama.cpp/pull/138), `IQ4_KSS` [PR 89](https://github.com/ikawrakow/ik_llama.cpp/pull/89), `IQ2_KS` [PR 85](https://github.com/ikawrakow/ik_llama.cpp/pull/85), `IQ4_KS` [PR 83](https://github.com/ikawrakow/ik_llama.cpp/pull/83), `IQ6_K` [PR 14](https://github.com/ikawrakow/ik_llama.cpp/pull/14), `IQ2_K, IQ3_K and IQ5_K` [PR 7](https://github.com/ikawrakow/ik_llama.cpp/pull/7), `IQ4_K` [PR 6](https://github.com/ikawrakow/ik_llama.cpp/pull/6)
+
+#### 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 and support improvements and fixes
+
+* MMQ implementation for `IQ4_KS_R4` and `IQ5_KS_R4` [PR 493](https://github.com/ikawrakow/ik_llama.cpp/pull/493)
+* CUDA implementation for `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)
+* 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)
+* CUDA GEMM and GEMV for `IQ4_KS_R4` and `IQ5_KS_R4` [PR 462](https://github.com/ikawrakow/ik_llama.cpp/pull/462)
+* CUDA implementation for `IQ2_K_R4`, `IQ3_K_R4`, `IQ4_K_R4`, `IQ5_K_R4` [PR 461](https://github.com/ikawrakow/ik_llama.cpp/pull/461)
+* Faster `IQ3_KT` and `IQ4_KT` [PR 453](https://github.com/ikawrakow/ik_llama.cpp/pull/453)
+* 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)
+* Zen4: Faster PP for `IQ2_KS, IQ4_KS, IQ5_KS` [PR 428](https://github.com/ikawrakow/ik_llama.cpp/pull/428)
+* CUDA: quantized GEMMs `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)
+
+### Features
+
+* 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)
+* June 7 2025: Add an endpoint that lists all the saved prompt caches to server [PR 502](https://github.com/ikawrakow/ik_llama.cpp/pull/502)
+* June 6 2025: Make prompt cache saving and restoring MLA aware [PR 497](https://github.com/ikawrakow/ik_llama.cpp/pull/497)
+* June 3 2025: Added samplers, XTC [PR 486](https://github.com/ikawrakow/ik_llama.cpp/pull/486), top-n σ [PR 489](https://github.com/ikawrakow/ik_llama.cpp/pull/489).
+* May 22 2025: Refactor `iqk_mul_mat.cpp` which speeds up compilation time significantly. [PR 435](https://github.com/ikawrakow/ik_llama.cpp/pull/435)
+* May 17 2025: Option to enable or disable the CPU FA kernels [PR 429](https://github.com/ikawrakow/ik_llama.cpp/pull/429).
 * May 12 2025: User can now control if/which operations with tensors held in RAM are offloaded to the GPU. See [PR 405](https://github.com/ikawrakow/ik_llama.cpp/pull/405) 
 * May 12 2025: Compatibility issues with mainline `llama.cpp` GGUFs for DeepSeek models with MLA enabled were resolved in [PR 394](https://github.com/ikawrakow/ik_llama.cpp/pull/394). The lower prompt processing performance resulting from using `llama.cpp`-style MLA GGUFs was recovered in [PR 409](https://github.com/ikawrakow/ik_llama.cpp/pull/409).
-* May 11 2025: 🚀 Slightly faster flash attention for DeepSeek models on CUDA, along with extending compatibility to Touring or newer GPUs. See [PR 408](https://github.com/ikawrakow/ik_llama.cpp/pull/408)
-* May 9 2025: Support for LlaMA-3-Nemotron models added, see [PR 377](https://github.com/ikawrakow/ik_llama.cpp/pull/377)
-* May 7 2025: 🚀 Faster TG for DeepSeek models with GPU or hybrid GPU/CPU inference. See [PR 386](https://github.com/ikawrakow/ik_llama.cpp/pull/386) for details. Caveat: Ampere or newer Nvidia GPU required
-* May 4 2025: 🚀 Significant token generation performance improvement on CUDA with Flash Attention for GQA models. For details and benchmarks see [PR #370](https://github.com/ikawrakow/ik_llama.cpp/pull/370) 
-* April 29 2025: Qwen3 support added, see [PR 355](https://github.com/ikawrakow/ik_llama.cpp/pull/355)
-* April 26 2025: GLM-4 support added, see [PR 344](https://github.com/ikawrakow/ik_llama.cpp/pull/344)
-* April 26 2025: Command-A support added, see [PR 341](https://github.com/ikawrakow/ik_llama.cpp/pull/341)
-* April 22 2025: Support for the latest Microsoft Bitnet model added, see [PR 337](https://github.com/ikawrakow/ik_llama.cpp/pull/337)
 * April 21 2025: ik_llama.cpp builds and runs successfully on Android (using termux), see [PR 336](https://github.com/ikawrakow/ik_llama.cpp/pull/336)
-* April 17 2025: 🚀 Better CPU Flash Attention token generation performance, see [PR 332](https://github.com/ikawrakow/ik_llama.cpp/pull/332)
-* April 13 2025: `IQ1_M` quantization improvements, see [PR 327](https://github.com/ikawrakow/ik_llama.cpp/pull/327)
-* April 10 2025: LLaMA-4 support added, see [PR 321](https://github.com/ikawrakow/ik_llama.cpp/pull/321). In the PR there are also some custom quantization recipes for L4-Scout provided.
-* April 7 2025: `IQ2_XS` quantization improvements, see [PR 312](https://github.com/ikawrakow/ik_llama.cpp/pull/312)
-* April 3 2025: 🚀 Much faster MoE implementation on Metal, see [PR 307](https://github.com/ikawrakow/ik_llama.cpp/pull/307) 
-* April 1 2025: Quantization improvements for `Q2_K, Q4_K, Q5_K, Q4_1, Q5_1`, see [PR 302](https://github.com/ikawrakow/ik_llama.cpp/pull/302)
-* March 28 2025: Quantization imrovements for `Q4_0, Q5_0, Q6_0, Q3_K, Q6_K, IQ4_XS, IQ4_NL`, see [PR 295](https://github.com/ikawrakow/ik_llama.cpp/pull/295)
-* March 25 2025: 🚀 Better MoE performance on CUDA
-* March 23 2025: 🚀 Better batched processing speed for DeepSeek models
-* March 22 2025: Gemma3 support added
-* March 21 2025: 🚀 FlashMLA-3: fastest CPU-only inference for DeepSeek models
-* March 18 2025: Reduce compute buffer size
-* March 17 2025: 🚀 FlashMLA-2 performance improvements
-* March 12 2025: Allow `Q8_0` KV cache with FlashMLA-2 on CUDA
-* March 10 2025: 🚀 Better TG performance for MoE models on CUDA
-* March 9 2025: 🚀 FlashMLA on CUDA
-* March 8 2025: 🚀 Faster FlashMLA CPU implementation
-* March 7 2025: Custom quantization mixes using regular expressions
-* March 5 2025: 🚀 FlashMLA on CUDA
-* March 3 2025: 🚀 Introducing FlashMLA - MLA with Flash Attention
-* March 1 2025: Smart Expert Reduction for faster DeepSeek inference
-* Feb 27 2025: MLA without transposed cache
-* Feb 25 2025: Tensor overrides for better control where model weights are stored (GPU or CPU)
-* Feb 23 2025: 🚀 Fused FFN ops for faster MoE inference
-* Feb 23 2025: `sweep-bench` - better performance benchmarking
-* Feb 20 2025: 🚀 Fast GEMM/GEMV for `IQ1_S`
-* Feb 19 2025: `Q8_KV` - new type for 8-bit KV-cache quantization
-* Feb 13 2025: Allow `Q8_0` quantized cache with MLA
-* Feb 11 2025: 🚀 Flash Attention support for DeepSeek models
-* Feb 9 2025: 🚀 MLA for DeepSeek models
-* Jan 23 2025: DeepSeek-V3 support added
+* March 1 2025: Smart Expert Reduction for faster DeepSeek inference [PR 239](https://github.com/ikawrakow/ik_llama.cpp/pull/239) 
+* Feb 25 2025: Tensor overrides for better control where model weights are stored (GPU or CPU) [PR 232](https://github.com/ikawrakow/ik_llama.cpp/pull/232)
+* Feb 23 2025: `sweep-bench` - better performance benchmarking [PR 225](https://github.com/ikawrakow/ik_llama.cpp/pull/225)
+* Feb 19 2025: `Q8_KV` - new type for 8-bit KV-cache quantization [PR 208](https://github.com/ikawrakow/ik_llama.cpp/pull/208)
+* March 7 2025: Custom quantization mixes using regular expressions [PR 244](https://github.com/ikawrakow/ik_llama.cpp/pull/244)
+
+### Performance improvements
+
+* 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 7 2025: Faster TG for DeepSeek models with GPU or hybrid GPU/CPU inference. [PR 386](https://github.com/ikawrakow/ik_llama.cpp/pull/386). Caveat: Ampere or newer Nvidia GPU required
+* 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) 
+* April 17 2025: Better CPU Flash Attention token generation performance. [PR 332](https://github.com/ikawrakow/ik_llama.cpp/pull/332)
+* April 3 2025: Much faster MoE implementation on Metal. [PR 307](https://github.com/ikawrakow/ik_llama.cpp/pull/307) 
+* March 25 2025: Better MoE performance on CUDA [PR 283](https://github.com/ikawrakow/ik_llama.cpp/pull/283)
+* March 23 2025: Better batched processing speed for DeepSeek models [PR 282](https://github.com/ikawrakow/ik_llama.cpp/pull/282)
+* March 18 2025: Reduce compute buffer size [PR 237](https://github.com/ikawrakow/ik_llama.cpp/pull/237)
+* March 10 2025: Better TG performance for MoE models on CUDA [PR 248](https://github.com/ikawrakow/ik_llama.cpp/pull/248)
+* Feb 23 2025: Fused FFN ops for faster MoE inference [PR 229](https://github.com/ikawrakow/ik_llama.cpp/pull/229)
+* Feb 20 2025: Fast GEMM/GEMV for `IQ1_S` [PR 212](https://github.com/ikawrakow/ik_llama.cpp/pull/212)
+
+### Flash-MLA
+
+* March 21 2025: 🚀 FlashMLA-3: fastest CPU-only inference for DeepSeek models [PR 273](https://github.com/ikawrakow/ik_llama.cpp/pull/273)
+* March 17 2025: 🚀 FlashMLA-2 performance improvements [PR 253](https://github.com/ikawrakow/ik_llama.cpp/pull/253)
+* March 12 2025: Allow `Q8_0` KV cache with FlashMLA-2 on CUDA [PR 265](https://github.com/ikawrakow/ik_llama.cpp/pull/265)
+* March 9 2025: 🚀 FlashMLA on CUDA [PR 247](https://github.com/ikawrakow/ik_llama.cpp/pull/247)
+* March 8 2025: 🚀 Faster FlashMLA CPU implementation [PR 243](https://github.com/ikawrakow/ik_llama.cpp/pull/243)
+* March 3 2025: 🚀 Introducing FlashMLA - MLA with Flash Attention [PR 240](https://github.com/ikawrakow/ik_llama.cpp/pull/240)
+* Feb 27 2025: MLA without transposed cache [PR 235](https://github.com/ikawrakow/ik_llama.cpp/pull/235)
+* Feb 13 2025: Allow `Q8_0` quantized cache with MLA [PR 206](https://github.com/ikawrakow/ik_llama.cpp/pull/206)
+* Feb 11 2025: 🚀 Flash Attention support for DeepSeek models [PR 200](https://github.com/ikawrakow/ik_llama.cpp/pull/200)
+* Feb 9 2025: 🚀 MLA for DeepSeek models [PR 188](https://github.com/ikawrakow/ik_llama.cpp/pull/188)
+
+### Fixes
+
+* Fix bug in MMVQ kernel [PR 446](https://github.com/ikawrakow/ik_llama.cpp/pull/446)
+* Fix AVX2 implementation of `IQ4_K, IQ4_KS, IQ5_K, IQ6_K` [PR 427](https://github.com/ikawrakow/ik_llama.cpp/pull/427) 
+* Fix standard attention on the CPU [PR 421](https://github.com/ikawrakow/ik_llama.cpp/pull/421) 
+* Fix imatrix calculation for MLA models [PR 411](https://github.com/ikawrakow/ik_llama.cpp/pull/411)
+* Fix new CUDA FA on Touring [PR 413](https://github.com/ikawrakow/ik_llama.cpp/pull/413)
+* Fix SER. CPU: [PR 415](https://github.com/ikawrakow/ik_llama.cpp/pull/415) CUDA: [PR 416](https://github.com/ikawrakow/ik_llama.cpp/pull/416)
 
 ## Resources