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⚡ fix typo

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doc/en/DeepseekR1_V3_tutorial.md
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@ -6,8 +6,8 @@ GPU: 4090D 24G VRAM <br>
## Bench Result
### V0.2
#### Settings
- Model: DeepseekV3-q4km（int4）<br>
- CPU: cpu_model_name：Intel (R) Xeon (R) Gold 6454S, 32 cores per socket, 2 socket, 2 numa nodes
- Model: DeepseekV3-q4km (int4)<br>
- CPU: cpu_model_name: Intel (R) Xeon (R) Gold 6454S, 32 cores per socket, 2 sockets, 2 numa nodes
- GPU: 4090D 24G VRAM
- We test after enough warm up
#### Memory consumption:
@ -16,7 +16,7 @@ GPU: 4090D 24G VRAM <br>
#### Benchmark Results
"6 experts" case is part of v0.3's preview
"6 experts" case is part of V0.3's preview
| Prompt<br>(500 tokens) | Dual socket Ktrans (6 experts) | Dual socket Ktrans (8 experts) | Single socket Ktrans (6 experts) | Single socket Ktrans (8 experts)| llama.cpp (8 experts) |
| --- | --- | --- | --- | --- | --- |
@ -28,7 +28,7 @@ GPU: 4090D 24G VRAM <br>
### V0.3-Preview
#### Settings
- Model: DeepseekV3-BF16 (online quant into int8 for CPU and int4 for GPU)
- CPU: cpu_model_name：Intel(R) Xeon(R) Gold 6454S, 32 cores per socket, 2 socket, 2 numa nodes
- CPU: cpu_model_name: Intel (R) Xeon (R) Gold 6454S, 32 cores per socket, 2 socket, 2 numa nodes
- GPU: (1~4)x 4090D 24GVRAM (requires more VRAM for longer prompt)
#### Memory consumptions:
@ -56,28 +56,28 @@ is speed up which is inspiring. So our showcase makes use of this finding*
## How to Run
### V0.2 Showcase
#### Single socket version (32 cores)
our local_chat test command is:
Our local_chat test command is:
``` shell
git clone https://github.com/kvcache-ai/ktransformers.git
cd ktransformers
numactl -N 1 -m 1 python ./ktransformers/local_chat.py --model_path <your model path> --gguf_path <your gguf path> --prompt_file <your promt txt file> --cpu_infer 33 --cache_lens 1536
numactl -N 1 -m 1 python ./ktransformers/local_chat.py --model_path <your model path> --gguf_path <your gguf path> --prompt_file <your prompt txt file> --cpu_infer 33 --cache_lens 1536
<when you see chat, then press enter to load the text prompt_file>
```
\<your model path\> can be local or set from onlie hugging face like deepseek-ai/DeepSeek-V3. If onlie encounters connection problem, try use mirror(hf-mirror.com) <br>
\<your gguf path\> can also be onlie, but as its large we recommend you download it and quantize the model to what you want <br>
The command numactl -N 1 -m 1 aims to adoid data transfer between numa nodes
\<your model path\> can be local or set from online hugging face like deepseek-ai/DeepSeek-V3. If online encounters connection problem, try use mirror (hf-mirror.com) <br>
\<your gguf path\> can also be online, but as its large we recommend you download it and quantize the model to what you want <br>
The command numactl -N 1 -m 1 aims to advoid data transfer between numa nodes
#### Dual socket version (64 cores)
Make suer before you install (use install.sh or `make dev_install`), setting the env var `USE_NUMA=1` by `export USE_NUMA=1` (if already installed, reinstall it with this env var set) <br>
our local_chat test command is:
Our local_chat test command is:
``` shell
git clone https://github.com/kvcache-ai/ktransformers.git
cd ktransformers
export USE_NUMA=1
make dev_install # or sh ./install.sh
python ./ktransformers/local_chat.py --model_path <your model path> --gguf_path <your gguf path> --prompt_file <your promt txt file> --cpu_infer 65 --cache_lens 1536
python ./ktransformers/local_chat.py --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>
```
The parameters meaning is the same. But As we use dual socket, so we set cpu_infer to 65
The parameters' meaning is the same. But As we use dual socket, we set cpu_infer to 65
## Some Explanations
1. Also we want to make further use of our two NUMA nodes on Xeon Gold cpu.
To avoid the cost of data transfer between nodes, we "copy" the critical matrix on