7d4833bce9
### What this PR does / why we need it? This PR refactors the tutorial documentation by restructuring it into three categories: Models, Features, and Hardware. This improves the organization and navigation of the tutorials, making it easier for users to find relevant information. - The single `tutorials/index.md` is split into three separate index files: - `docs/source/tutorials/models/index.md` - `docs/source/tutorials/features/index.md` - `docs/source/tutorials/hardwares/index.md` - Existing tutorial markdown files have been moved into their respective new subdirectories (`models/`, `features/`, `hardwares/`). - The main `index.md` has been updated to link to these new tutorial sections. This change makes the documentation structure more logical and scalable for future additions. ### Does this PR introduce _any_ user-facing change? Yes, this PR changes the structure and URLs of the tutorial documentation pages. Users following old links to tutorials will encounter broken links. It is recommended to set up redirects if the documentation framework supports them. ### How was this patch tested? These are documentation-only changes. The documentation should be built and reviewed locally to ensure all links are correct and the pages render as expected. - vLLM version: v0.15.0 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.15.0 Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
109 lines
2.9 KiB
Markdown
109 lines
2.9 KiB
Markdown
# Kimi-K2-Thinking
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## Run with Docker
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```{code-block} bash
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:substitutions:
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# Update the vllm-ascend image
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export IMAGE=m.daocloud.io/quay.io/ascend/vllm-ascend:|vllm_ascend_version|
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export NAME=vllm-ascend
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# Run the container using the defined variables
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# Note: If you are running bridge network with docker, please expose available ports for multiple nodes communication in advance
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docker run --rm \
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--name $NAME \
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--net=host \
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--shm-size=1g \
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--device /dev/davinci0 \
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--device /dev/davinci1 \
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--device /dev/davinci2 \
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--device /dev/davinci3 \
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--device /dev/davinci4 \
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--device /dev/davinci5 \
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--device /dev/davinci6 \
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--device /dev/davinci7 \
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--device /dev/davinci8 \
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--device /dev/davinci9 \
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--device /dev/davinci10 \
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--device /dev/davinci11 \
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--device /dev/davinci12 \
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--device /dev/davinci13 \
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--device /dev/davinci14 \
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--device /dev/davinci15 \
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--device /dev/davinci_manager \
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--device /dev/devmm_svm \
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--device /dev/hisi_hdc \
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-v /usr/local/dcmi:/usr/local/dcmi \
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-v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
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-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
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-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
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-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
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-v /etc/ascend_install.info:/etc/ascend_install.info \
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-v /mnt/sfs_turbo/.cache:/home/cache \
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-it $IMAGE bash
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```
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## Verify the Quantized Model
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Please be advised to edit the value of `"quantization_config.config_groups.group_0.targets"` from `["Linear"]` into `["MoE"]` in `config.json` of original model downloaded from [Hugging Face](https://huggingface.co/moonshotai/Kimi-K2-Thinking).
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```json
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{
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"quantization_config": {
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"config_groups": {
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"group_0": {
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"targets": [
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"MoE"
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]
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}
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}
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}
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}
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```
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Your model files look like:
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```bash
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.
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|-- chat_template.jinja
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|-- config.json
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|-- configuration_deepseek.py
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|-- configuration.json
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|-- generation_config.json
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|-- model-00001-of-000062.safetensors
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|-- ...
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|-- model-00062-of-000062.safetensors
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|-- model.safetensors.index.json
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|-- modeling_deepseek.py
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|-- tiktoken.model
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|-- tokenization_kimi.py
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`-- tokenizer_config.json
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```
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## Online Inference on Multi-NPU
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Run the following script to start the vLLM server on Multi-NPU:
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For an Atlas 800 A3 (64G*16) node, tensor-parallel-size should be at least 16.
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```bash
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vllm serve Kimi-K2-Thinking \
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--served-model-name kimi-k2-thinking \
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--tensor-parallel-size 16 \
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--enable_expert_parallel \
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--trust-remote-code \
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--no-enable-prefix-caching
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```
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Once your server is started, you can query the model with input prompts.
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```bash
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curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
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"model": "kimi-k2-thinking",
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"messages": [
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{"role": "user", "content": "Who are you?"}
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],
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"temperature": 1.0
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}'
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```
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