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ms-swift: Use PEFT or Full-parameter to finetune 250+ LLMs or 25+ MLLMs

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Thanks for your work! Since SWIFT doesn't support deployment (API server) for multimodal models, can I train models using SWIFT and deploy to other frameworks such as LMDeploy? Thanks!

你好,我已按照说明将ColossalAI版本的grok-1下载到本地,并使用项目脚本训练。然而,我发现模型载入时间很长(50分钟左右),而我使用ColossalAI的载入脚本只需要20分钟左右。请问这是为什么?我应该如何调整以快速载入?脚本设置如下: nproc_per_node=8 PYTHONPATH=../../.. \ model_dir="" data_dir="" torchrun \ --nproc_per_node=$nproc_per_node \ --master_port 29500 \ llm_sft.py \ --model_type grok-1 \ --model_id_or_path $model_dir \ --sft_type lora \ --tuner_backend peft \ --dtype bf16 \...

**Describe the bug** 目前使用以下脚本部署internvl-v15显示部署成功: CUDA_VISIBLE_DEVICES=0 swift deploy \ --model_type internvl-chat-v1_5 \ --model_id_or_path /home/weight/internvl-chat-v1-5/ \ --use_flash_attn true \ --dtype bf16 使用openai格式脚本调用,可以获取模型名称,然后就报错: ```python from openai import OpenAI import base64 import time client...

已经运行了5分钟,一条数据也没有跑出来 ![image](https://github.com/modelscope/swift/assets/47747764/758af88a-d8f5-4a0a-a8b5-9146f70fcbeb)

请问这种应该怎么做?

swift export 对百川2进行awq量化 指定 --tensor_parallel_size --gpu_memory_utilization 感觉根本不管用,指定4张卡,只会用两张卡,然后就是爆显存

While copying the parameter named "base_model.model.model.layers.78.self_attn.v_proj.lora_B.default.weight", whose dimensions in the model are torch.Size([8192, 8]) and whose dimensions in the checkpoint are torch.Size([8192, 8]), an exception occurred : ('get_p2p_access:torch_npu/csrc/core/npu/NPUPeerToPeerAccess.cpp:80 NPU error,...

hi, I installed the required environment following the steps, and then directly ran the Lora fine-tuning script for llama3-8b-instruct, but the following error occurred: Traceback (most recent call last): File...

**Describe the feature** Please describe the feature requested here(请在这里描述需求) RLAIF-V effectively reduce the hallucination of different MLLMs **Paste any useful information** Paste any useful information, including papers, github links, etc.(请在这里描述其他有用的信息,比如相关的论文地址,github链接等)...

我使用 llava 72B 推理时,batch_size=1 显卡利用率不高,怎么可以使用大 batch 推理提高利用率呢?我没有使用 vllm,是直接使用的 swift 推理 https://github.com/modelscope/swift/blob/main/docs/source/Multi-Modal/llava%E6%9C%80%E4%BD%B3%E5%AE%9E%E8%B7%B5.md