and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron. - Prefix caching support - Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) split.json --enable- →chunked-prefill --max-num-batched-tokens 256 ``` ## 1.3.5 Limitations - LoRA serving is not supported. - Only LLM models are currently supported. LLaVa and encoder-decoder models = parser.parse_args() main(args) ## 1.10.8 Lora With Quantization Inference Source https://github.com/vllm-project/vllm/blob/main/examples/lora_with_quantization_inference.py. ```python """

and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron. - Prefix caching support - Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) split.json --enable- →chunked-prefill --max-num-batched-tokens 256 ``` ## 1.3.5 Limitations - LoRA serving is not supported. - Only LLM models are currently supported. LLaVa and encoder-decoder models = parser.parse_args() main(args) ## 1.10.8 Lora With Quantization Inference Source https://github.com/vllm-project/vllm/blob/main/examples/lora_with_quantization_inference.py. ```python """

and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron. - Prefix caching support - Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) split.json --enable- →chunked-prefill --max-num-batched-tokens 256 ``` ## 1.3.5 Limitations - LoRA serving is not supported. - Only LLM models are currently supported. LLaVa and encoder-decoder models = parser.parse_args() main(args) ## 1.10.8 Lora With Quantization Inference Source https://github.com/vllm-project/vllm/blob/main/examples/lora_with_quantization_inference.py. ```python """

Support NVIDIA GPUs and AMD GPUs - (Experimental) Prefix caching support - (Experimental) Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) com/vllm-project/vllm.git $ cd vllm $ # export VLLM_INSTALL_PUNICA_KERNELS=1 # optionally build for multi-LoRA capability $ pip install -e . # This may take 5-10 minutes. ``` Tip: Building from source requires parser.parse_args() main(args) ``` ## 1.6.7 Lora With Quantization Inference Source https://github.com/vllm-project/vllm/blob/main/examples/lora_with_quantization_inference.py. ```python """

Support NVIDIA GPUs and AMD GPUs - (Experimental) Prefix caching support - (Experimental) Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) com/vllm-project/vllm.git $ cd vllm $ # export VLLM_INSTALL_PUNICA_KERNELS=1 # optionally build for multi-LoRA capability $ pip install -e . # This may take 5-10 minutes. ``` Tip: Building from source requires parser.parse_args() main(args) ``` ## 1.8.7 Lora With Quantization Inference Source https://github.com/vllm-project/vllm/blob/main/examples/lora_with_quantization_inference.py. ```python """

PowerPC CPUs, TPU, and AWS Trainium and Inferentia Accelerators. - Prefix caching support - Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) split.json --enable- →chunked-prefill --max-num-batched-tokens 256 ``` ## 1.3.5 Limitations - LoRA serving is not supported. - Only LLM models are currently supported. LLaVa and encoder-decoder models parser.parse_args() main(args) ``` ## 1.10.8 Lora With Quantization Inference Source https://github.com/vllm-project/vllm/blob/main/examples/lora_with_quantization_inference.py. ```python """

Support NVIDIA GPUs and AMD GPUs - (Experimental) Prefix caching support - (Experimental) Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) com/vllm-project/vllm.git $ cd vllm $ # export VLLM_INSTALL_PUNICA_KERNELS=1 # optionally build for multi-LoRA capability $ pip install -e . # This may take 5-10 minutes. ``` Tip: Building from source requires _split.json --enable- →chunked-prefill --max-num-batched-tokens 256 ``` ## 1.3.5 Limitations - LoRA serving is not supported. - Only LLM models are currently supported. LLaVa and encoder-decoder models

Support NVIDIA GPUs and AMD GPUs - (Experimental) Prefix caching support - (Experimental) Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) com/vllm-project/vllm.git $ cd vllm $ # export VLLM_INSTALL_PUNICA_KERNELS=1 # optionally build for multi-LoRA capability $ pip install -e . # This may take 5-10 minutes. ``` Tip: Building from source requires _split.json --enable- →chunked-prefill --max-num-batched-tokens 256 ``` ## 1.3.5 Limitations - LoRA serving is not supported. - Only LLM models are currently supported. LLaVa and encoder-decoder models

__theme=dark` 然后享受使用 Qwen 的 Web UI 吧! #### 1.6.2 下一步 TGW 中包含了许多更多用途，您甚至可以在其中享受角色扮演的乐趣，并使用不同类型的量化模型。您可以训练诸如 LoRA 这样的算法，并将 Stable Diffusion 和 Whisper 等扩展功能纳入其中。赶快去探索更多高级用法，并将它们应用于 Qwen 模型中吧！ ### 1.7 AWQ 对于量化模型，我们推荐使用 的训练脚本修改而来的。这个脚本用于使用 Hugging Face Trainer 对 Qwen 模型进行微调。你可以在以下链接查看这个脚本：这里。这个脚本具有以下特点： · 支持单卡和多卡分布式训练 • 支持全参数微调、LoRA 以及 Q-LoRA。 下面，我们介绍脚本的更多细节。 ## 安装 开始之前，确保你已经安装了以下代码库： pip install peft deepspeed optimum accelerate 例如单 GPU 训练、多 GPU 训练、全参数微调、LoRA 或 Q-LoRA），您可能需要不同的超参数设置。 cd examples/sft bash finetune.sh -m -d --deepspeed [--use_lora_True] [--q_lora True] 为您的模型指定 ，为您的数据指定

and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron. - Prefix caching support - Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) split.json --enable- →chunked-prefill --max-num-batched-tokens 256 ``` ## 1.3.5 Limitations - LoRA serving is not supported. - Only LLM models are currently supported. LLaVa and encoder-decoder models = parser.parse_args() main(args) ## 1.10.8 Lora With Quantization Inference Source https://github.com/vllm-project/vllm/blob/main/examples/lora_with_quantization_inference.py. ```python """