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README.md
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@ -23,6 +23,7 @@ Our vision for KTransformers is to serve as a flexible platform for experimentin
<h2 id="Updates">🔥 Updates</h2>
* **Mar 5, 2025**: Support unsloth 1.58/2.51 bits weights and [IQ1_S/FP8 hybrid](./doc/en/fp8_kernel.md) weights. Support 139K [Longer Context](./doc/en/DeepseekR1_V3_tutorial.md#v022-longer-context) for DeepSeek-V3 and R1 in 24GB VRAM.
* **Feb 25, 2025**: Support [FP8 GPU kernel](./doc/en/fp8_kernel.md) for DeepSeek-V3 and R1; [Longer Context](./doc/en/DeepseekR1_V3_tutorial.md#v022-longer-context).
* **Feb 15, 2025**: Longer Context (from 4K to 8K for 24GB VRAM) & Slightly Faster Speed （+15%, up to 16 Tokens/s), update [docs](./doc/en/DeepseekR1_V3_tutorial.md) and [online books](https://kvcache-ai.github.io/ktransformers/).
* **Feb 10, 2025**: Support Deepseek-R1 and V3 on single (24GB VRAM)/multi gpu and 382G DRAM, up to 3~28x speedup. For detailed show case and reproduction tutorial, see [here](./doc/en/DeepseekR1_V3_tutorial.md).

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doc/README.md
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@ -22,6 +22,7 @@ Our vision for KTransformers is to serve as a flexible platform for experimentin
<h2 id="Updates">🔥 Updates</h2>
* **Mar 5, 2025**: Support unsloth 1.58/2.51 bits weights and [IQ1_S/FP8 hybrid](./doc/en/fp8_kernel.md) weights. Support 139K [Longer Context](./doc/en/DeepseekR1_V3_tutorial.md#v022-longer-context) for DeepSeek-V3 and R1 in 24GB VRAM.
* **Feb 25, 2025**: Support [FP8 GPU kernel](./doc/en/fp8_kernel.md) for DeepSeek-V3 and R1; [Longer Context](./doc/en/DeepseekR1_V3_tutorial.md#v022-longer-context).
* **Feb 10, 2025**: Support Deepseek-R1 and V3 on single (24GB VRAM)/multi gpu and 382G DRAM, up to 3~28x speedup. The detailed tutorial is [here](./en/DeepseekR1_V3_tutorial.md).
* **Aug 28, 2024**: Support 1M context under the InternLM2.5-7B-Chat-1M model, utilizing 24GB of VRAM and 150GB of DRAM. The detailed tutorial is [here](./en/long_context_tutorial.md).

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doc/en/DeepseekR1_V3_tutorial.md
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@ -16,7 +16,7 @@
- [Memory consumptions:](#memory-consumptions)
- [Benchmark results](#benchmark-results-2)
- [How to Run](#how-to-run)
- [V0.2.2 longer context \& FP8 kernel](#v022-longer-context--fp8-kernel)
- [v0.2.2 \& v0.2.3 longer context \& FP8 kernel](#v022--v023-longer-context--fp8-kernel)
- [longer context](#longer-context)
- [FP8 kernel](#fp8-kernel)
- [V0.2 \& V0.2.1 Showcase](#v02--v021-showcase)
@ -157,7 +157,7 @@ the output quality doesn't change. But the speed of decoding and prefill
is speed up which is inspiring. So our showcase makes use of this finding*
## How to Run
### V0.2.2 longer context & FP8 kernel
### v0.2.2 & v0.2.3 longer context & FP8 kernel
#### longer context
To use this feature, [install flashinfer](https://github.com/flashinfer-ai/flashinfer) first.