support vllm

Former-commit-id: 889f6e910e654d8ec3922c2185042d737ffbf1c3

README.md

@@ -47,10 +47,11 @@ Choose your path:
 
 - **Various models**: LLaMA, Mistral, Mixtral-MoE, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, etc.
 - **Integrated methods**: (Continuous) pre-training, supervised fine-tuning, reward modeling, PPO and DPO.
-- **Scalable resources**: 32-bit full-tuning, 16-bit freeze-tuning, 16-bit LoRA, 2/4/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8.
-- **Advanced algorithms**: DoRA, LongLoRA, LLaMA Pro, LoftQ, agent tuning.
-- **Practical tricks**: FlashAttention-2, Unsloth, RoPE scaling, NEFTune, rsLoRA.
+- **Scalable resources**: 32-bit full-tuning, 16-bit freeze-tuning, 16-bit LoRA and 2/4/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8.
+- **Advanced algorithms**: DoRA, LongLoRA, LLaMA Pro, LoftQ and Agent tuning.
+- **Practical tricks**: FlashAttention-2, Unsloth, RoPE scaling, NEFTune and rsLoRA.
 - **Experiment monitors**: LlamaBoard, TensorBoard, Wandb, MLflow, etc.
+- **Faster inference**: OpenAI-style API, Gradio UI and CLI with vLLM worker.
 
 ## Benchmark
 
@@ -69,6 +70,8 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
 
 ## Changelog
 
+[24/03/07] We integrated [vLLM](https://github.com/vllm-project/vllm) for faster and concurrent inference. Try `--infer_backend vllm` to enjoy **270%** inference speed. (LoRA is not yet supported, merge it first.)
+
 [24/02/28] We supported weight-decomposed LoRA (**[DoRA](https://arxiv.org/abs/2402.09353)**). Try `--use_dora` to activate DoRA training.
 
 [24/02/15] We supported **block expansion** proposed by [LLaMA Pro](https://github.com/TencentARC/LLaMA-Pro). See `scripts/llama_pro.py` for usage.
@@ -79,7 +82,7 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
 
 [24/01/18] We supported **agent tuning** for most models, equipping model with tool using abilities by fine-tuning with `--dataset glaive_toolcall`.
 
-[23/12/23] We supported **[unsloth](https://github.com/unslothai/unsloth)**'s implementation to boost LoRA tuning for the LLaMA, Mistral and Yi models. Try `--use_unsloth` argument to activate unsloth patch. It achieves 1.7x speed in our benchmark, check [this page](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison) for details.
+[23/12/23] We supported **[unsloth](https://github.com/unslothai/unsloth)**'s implementation to boost LoRA tuning for the LLaMA, Mistral and Yi models. Try `--use_unsloth` argument to activate unsloth patch. It achieves **170%** speed in our benchmark, check [this page](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison) for details.
 
 [23/12/12] We supported fine-tuning the latest MoE model **[Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)** in our framework. See hardware requirement [here](#hardware-requirement).
 
@@ -553,7 +556,7 @@ deepspeed --num_gpus 8 src/train_bash.py \
 ### Merge LoRA weights and export model
 
 ```bash
-python src/export_model.py \
+CUDA_VISIBLE_DEVICES=0 python src/export_model.py \
     --model_name_or_path path_to_llama_model \
     --adapter_name_or_path path_to_checkpoint \
     --template default \
@@ -574,7 +577,7 @@ python src/export_model.py \
 ### Inference with OpenAI-style API
 
 ```bash
-python src/api_demo.py \
+CUDA_VISIBLE_DEVICES=0 python src/api_demo.py \
     --model_name_or_path path_to_llama_model \
     --adapter_name_or_path path_to_checkpoint \
     --template default \
@@ -587,7 +590,7 @@ python src/api_demo.py \
 ### Inference with command line
 
 ```bash
-python src/cli_demo.py \
+CUDA_VISIBLE_DEVICES=0 python src/cli_demo.py \
     --model_name_or_path path_to_llama_model \
     --adapter_name_or_path path_to_checkpoint \
     --template default \
@@ -597,7 +600,7 @@ python src/cli_demo.py \
 ### Inference with web browser
 
 ```bash
-python src/web_demo.py \
+CUDA_VISIBLE_DEVICES=0 python src/web_demo.py \
     --model_name_or_path path_to_llama_model \
     --adapter_name_or_path path_to_checkpoint \
     --template default \