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An Yang 2025-04-29 08:52:30 +00:00 committed by system
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@ -90,7 +90,7 @@ print("thinking content:", thinking_content)
print("content:", content)
```
For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.4` or to create an OpenAI-compatible API endpoint:
For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
- SGLang:
```shell
python -m sglang.launch_server --model-path Qwen/Qwen3-32B --reasoning-parser qwen3
@ -100,7 +100,7 @@ For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.4` or to create
vllm serve Qwen/Qwen3-32B --enable-reasoning --reasoning-parser deepseek_r1
```
For local use, applications such as llama.cpp, Ollama, LMStudio, and MLX-LM have also supported Qwen3.
For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
## Switching Between Thinking and Non-Thinking Mode
@ -274,7 +274,7 @@ YaRN is currently supported by several inference frameworks, e.g., `transformers
{
...,
"rope_scaling": {
"type": "yarn",
"rope_type": "yarn",
"factor": 4.0,
"original_max_position_embeddings": 32768
}
@ -286,12 +286,12 @@ YaRN is currently supported by several inference frameworks, e.g., `transformers
For `vllm`, you can use
```shell
vllm serve ... --rope-scaling '{"type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072
vllm serve ... --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072
```
For `sglang`, you can use
```shell
python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}'
python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}'
```
For `llama-server` from `llama.cpp`, you can use