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⚡ support R1 force thinking

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doc/en/DeepseekR1_V3_tutorial.md
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@ -19,6 +19,8 @@
- [Dual socket version (64 cores)](#dual-socket-version-64-cores-1)
- [Some Explanations](#some-explanations)
- [FAQ](#faq)
- [R1 No Thinking](#r1-no-thinking)
- [More FAQ](#more-faq)
# SUMMARY
@ -110,21 +112,30 @@ 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 prompt txt file> --cpu_infer 33 --cache_lens 1536
git submodule init
git submodule update
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 --max_new_tokens 1000
<when you see chat, then press enter to load the text prompt_file>
```
\<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
`<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 (notice it's the dir path) <br>
`--max_new_tokens 1000` is the max output token length. If you find the answer is truncated, you
can increase the number for longer answer (But be aware of OOM, and increase it will slow down the generation rate.).
<br>
The command numactl -N 1 -m 1 aims to advoid data transfer between numa nodes<br>
Attention! If you are testing R1 and it may skip thinking. So you can add arg: `--force_think true`. This is explained in [FAQ](#faq) part
#### 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:
``` shell
git clone https://github.com/kvcache-ai/ktransformers.git
cd ktransformers
git submodule init
git submodule update
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 prompt 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 --max_new_tokens 1000
<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, we set cpu_infer to 65
@ -135,7 +146,7 @@ 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
python -m ktransformers.local_chat --model_path <your model path> --gguf_path <your gguf path> --prompt_file <your prompt txt file> --cpu_infer 65 --max_new_tokens 1000
<when you see chat, then press enter to load the text prompt_file>
```
The parameters' meaning is the same with V0.2. But As we use dual socket, we set cpu_infer to 65
@ -160,4 +171,8 @@ DeepSeek's MLA operators are highly computationally intensive. While running eve
5. Why Intel CPUs?
Intel is currently the only CPU vendor that supports AMX-like instructions, which delivers significantly better performance compared to AVX-only alternatives.
## FAQ
### R1 No Thinking
Attention! If you are testing R1 and it may skip thinking. So you can add arg: `--force_think true`. The detail is in [FAQ](./FAQ.md) part <br>
### More FAQ
[See detail](./FAQ.md)