⚡ release v0.2.0

README.md

@@ -23,7 +23,7 @@ Our vision for KTransformers is to serve as a flexible platform for experimentin
 
 <h2 id="Updates">🔥 Updates</h2>
 
-* **Fed 10, 2025**: Support DeepseekR1 and V3 on single (24GB VRAM)/multi gpu and 382G DRAM, up to 3~64x speedup. The Detailed tutorial is [here](./doc/en/DeepseekR1_V3_tutorial.md)
+* **Fed 10, 2025**: Support Deepseek-R1 and V3 on single (24GB VRAM)/multi gpu and 382G DRAM, up to 3~64x speedup. The detailed tutorial is [here](./doc/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](./doc/en/long_context_tutorial.md).
 * **Aug 28, 2024**: Decrease DeepseekV2's required VRAM from 21G to 11G.
 * **Aug 15, 2024**: Update detailed [TUTORIAL](doc/en/injection_tutorial.md) for injection and multi-GPU. 
@@ -50,7 +50,7 @@ https://github.com/user-attachments/assets/ebd70bfa-b2c1-4abb-ae3b-296ed38aa285
  		- Compared to 4.51 tokens/s in llama.cpp with 2×32 cores, achieving up to **3.03× speedup**.  
 	- Upcoming Open Source Release:
 		- AMX optimizations and selective expert activation will be open-sourced in V0.3.  
-		- Currently available only in preview binary distribution, which can be found [here](xxx).  
+		- Currently available only in preview binary distribution, which can be downloaded [here](https://github.com/kvcache-ai/ktransformers/releases/download/v0.1.4/ktransformers-0.3.0rc0+cu126torch26fancy-cp311-cp311-linux_x86_64.whl).  
 
 - **Local 236B DeepSeek-Coder-V2:** Running its Q4_K_M version using only 21GB VRAM and 136GB DRAM, attainable on a local desktop machine, which scores even better than GPT4-0613 in [BigCodeBench](https://huggingface.co/blog/leaderboard-bigcodebench).
 

doc/en/DeepseekR1_V3_tutorial.md

@@ -23,8 +23,8 @@ https://github.com/user-attachments/assets/ebd70bfa-b2c1-4abb-ae3b-296ed38aa285
  		- Compared to 4.51 tokens/s in llama.cpp with 2×32 cores, achieving up to **3.03× speedup**.  
   
 
-But we're also previewing our upcoming optimizations, including an Intel AMX-accelerated kernel and a selective expert activation method, which will significantly enhance performance. With V0.3-preview, we achieve up to 286 tokens/s for prefill, making it up to **64× faster than llama.cpp** for local inference.
-The binary distribution is available now and the source code will come ASAP! Check out the details [here](xxx)  
+We also give our upcoming optimizations previews, including an Intel AMX-accelerated kernel and a selective expert activation method, which will significantly enhance performance. With V0.3-preview, we achieve up to 286 tokens/s for prefill, making it up to **64× faster than llama.cpp** for local inference.
+The binary distribution is available now and the source code will come ASAP! Check out the wheel package [here](https://github.com/kvcache-ai/ktransformers/releases/download/v0.1.4/ktransformers-0.3.0rc0+cu126torch26fancy-cp311-cp311-linux_x86_64.whl)  
 
 
 ## Prerequisites
@@ -111,6 +111,8 @@ The parameters' meaning is the same. But As we  use dual socket, we set cpu_infe
 #### Dual socket version (64 cores)
 Our local_chat test command is:
 ``` shell
+wget https://github.com/kvcache-ai/ktransformers/releases/download/v0.1.4/ktransformers-0.3.0rc0+cu126torch26fancy-cp311-cp311-linux_x86_64.whl
+pip install ./ktransformers-0.3.0rc0+cu126torch26fancy-cp311-cp311-linux_x86_64.whl
 python -m ktransformers.local_chat --model_path <your model path> --gguf_path <your gguf path>  --prompt_file <your prompt txt file>  --cpu_infer 65  --cache_lens 1536 
 <when you see chat, then press enter to load the text prompt_file>
 ```

pyproject.toml

@@ -23,7 +23,6 @@ dependencies = [
   "blessed >= 1.20.0",
   "accelerate >= 0.31.0",
   "sentencepiece >= 0.1.97",
-  "flash_attn == 2.7.4.post1",
   "setuptools",
   "ninja",
   "wheel",

requirements-local_chat.txt

@@ -1,6 +1,5 @@
 fire
 transformers==4.43.2
-flash_attn==2.7.4.post1
 numpy
 torch>=2.3.0
 packaging