Metadata-Version: 2.1
Name: ms-swift
Version: 1.1.0
Summary: Swift: Scalable lightWeight Infrastructure for Fine-Tuning
Home-page: https://github.com/modelscope/swift
Author: DAMO ModelScope teams
Author-email: contact@modelscope.cn
License: Apache License 2.0
Description: <h1>SWIFT(Scalable lightWeight Infrastructure for Fine-Tuning)</h1>
        
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        <a href="https://modelscope.cn/home">Modelscope Hub</a>
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                <a href="README_CN.md">中文</a>&nbsp ｜ &nbspEnglish
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        # Introduction
        
        SWIFT (Scalable lightWeight Infrastructure for Fine-Tuning) is an extensible framwork designed to faciliate lightweight model fine-tuning and inference. It integrates implementations for various efficient fine-tuning methods,  by embracing approaches that is parameter-efficient, memory-efficient, and time-efficient. SWIFT integrates seamlessly into ModelScope ecosystem and offers the capabilities to finetune various models, with a primary emphasis on LLMs and vision models. Additionally, SWIFT is fully compatible with [PEFT](https://github.com/huggingface/peft), enabling users to  leverage the familiar Peft interface to finetune ModelScope models.
        
        Currently supported approches (and counting):
        
        1. LoRA: [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/abs/2106.09685)
        2. Adapter: [Parameter-Efficient Transfer Learning for NLP](http://arxiv.org/abs/1902.00751)
        3. Prompt Tuning: [Visual Prompt Tuning](https://arxiv.org/abs/2203.12119)
        4. Side: [Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks](https://arxiv.org/abs/1912.13503)
        5. ResTuning-Bypass
        7. All tuners offered on [PEFT](https://github.com/huggingface/peft)
        
        Key features:
        
        1. By integrating the ModelScope library, models can be readily obatined via a model-id.
        2. Tuners provided by SWIFT can be combined together to allow exploration of multiple tuners on a model for best result.
        3. Support calling `activate_adapter` or `deactivate_adapter` or `set_active_adapters`  to activate/deactivate tuners. User can inference with one model and multiple tuners in different threads independently.
        
        Users can check the [documentation of Swift](docs/source/GetStarted/Introduction.md) to get detail tutorials.
        
        ## LLM SFT Example
        [code link](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm)
        
        1. supported SFT methods: [LoRA](https://arxiv.org/abs/2106.09685), [QLoRA](https://arxiv.org/abs/2305.14314), full(full parameter fine-tuning)
        2. supported models:
           1. qwen series: qwen-7b, [qwen-7b-chat](https://github.com/QwenLM/Qwen-7B)
           2. qwen-vl series: qwen-vl, [qwen-vl-chat](https://github.com/QwenLM/Qwen-VL)
           3. baichuan series: baichuan-7b, baichuan-13b, baichuan-13b-chat, baichuan2-7b, [baichuan2-7b-chat](https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Chat/summary), baichuan2-13b, baichuan2-13b-chat
           4. chatglm2 series: [chatglm2-6b](https://modelscope.cn/models/ZhipuAI/chatglm2-6b/summary), chatglm2-6b-32k
           5. llama series: llama2-7b, llama2-7b-chat, llama2-13b, llama2-13b-chat, llama2-70b, [llama2-70b-chat](https://modelscope.cn/models/modelscope/Llama-2-70b-chat-ms/summary)
           6. openbuddy-llama series: openbuddy-llama2-13b, openbuddy-llama-65b, [openbuddy-llama2-70b](https://modelscope.cn/models/OpenBuddy/openbuddy-llama2-70b-v10.1-bf16/summary)
           7. internlm series: internlm-7b, internlm-7b-chat, internlm-7b-chat-8k, [internlm-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm-20b/summary), [internlm-20b-chat](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm-chat-20b/summary)
           8. other: [polylm-13b](https://modelscope.cn/models/damo/nlp_polylm_13b_text_generation/summary), [seqgpt-560m](https://modelscope.cn/models/damo/nlp_seqgpt-560m/summary)
        3. supported features: quantization, DDP, model parallelism(device map), gradient checkpointing, gradient accumulation, pushing to modelscope hub, custom datasets, multimodal and agent SFT, mutli-round chat, ...
        4. supported datasets:
           1. NLP: [alpaca-en](https://modelscope.cn/datasets/AI-ModelScope/alpaca-gpt4-data-en/summary)(gpt4), [alpaca-zh](https://modelscope.cn/datasets/AI-ModelScope/alpaca-gpt4-data-zh/summary)(gpt4), finance-en, multi-alpaca-all, code-en, instinwild-en, instinwild-zh, cot-en, cot-zh, firefly-all-zh, poetry-zh, instruct-en, gpt4all-en, cmnli-zh, [jd-zh](https://modelscope.cn/datasets/DAMO_NLP/jd/summary), [dureader-robust-zh](https://modelscope.cn/datasets/modelscope/DuReader_robust-QG/summary), medical-en, medical-zh, medical-mini-zh, sharegpt-en, sharegpt-zh, [code-python-zh](https://modelscope.cn/datasets/codefuse-ai/CodeExercise-Python-27k/summary), [advertise-gen](https://modelscope.cn/datasets/lvjianjin/AdvertiseGen/summary)
           2. agent: [damo-agent-zh](https://modelscope.cn/datasets/damo/MSAgent-Bench/summary), damo-agent-mini-zh
           3. multi-modal: [coco-en](https://modelscope.cn/datasets/modelscope/coco_2014_caption/summary)
           4. other: [cls-fudan-news-zh](https://modelscope.cn/datasets/damo/zh_cls_fudan-news/files), [ner-jave-zh](https://modelscope.cn/datasets/damo/zh_ner-JAVE/summary)
        5. supported templates: chatml(qwen), baichuan, chatglm2, llama, openbuddy-llama, default, default-generation
        
        # Installation
        
        SWIFT is running in Python environment. Please make sure your python version is higher than 3.8.
        
        - Install SWIFT by the `pip` command:
        
        ```shell
        pip install ms-swift -U
        ```
        
        - Install SWIFT by source code(for running sft/infer examples), please run:
        
        ```shell
        git clone https://github.com/modelscope/swift.git
        cd swift
        pip install -e .
        ```
        
        SWIFT requires torch>=1.13.
        
        - Use SWIFT in our docker image:
        
        ```shell
        docker pull registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.8.0-py38-torch2.0.1-tf2.13.0-1.9.1
        ```
        
        # Getting Started
        
        SWIFT supports multiple tuners, as well as tuners provided by [PEFT](https://github.com/huggingface/peft). To use these tuners, simply call:
        
        ```python
        from swift import Swift, LoRAConfig
        config = LoRAConfig(...)
        model = Swift.prepare_model(model, config, extra_state_keys=['...'])
        ```
        
        The code snippet above initialized the tuner randomly. The input model is an instance of `torch.nn.Module`, the config is a subclass instance of `SwiftConfig` or `PeftConfig`. extra_state_keys is
        the extra module weights(like the linear head) to be trained and stored in the output dir.
        
        You may combine multiple tuners by:
        
        ```python
        from swift import Swift, LoRAConfig, PromptConfig
        model = Swift.prepare_model(model, {'lora': LoRAConfig(...), 'prompt': PromptConfig(...)})
        ```
        
        Call `save_pretrained` and `push_to_hub` after finetuning:
        
        ```python
        from swift import push_to_hub
        model.save_pretrained('some-output-folder')
        push_to_hub('my-group/some-repo-id-modelscope', 'some-output-folder', token='some-ms-token')
        ```
        Assume `my-group/some-repo-id-modelscope` is the model-id in the hub, and `some-ms-token` is the token for uploading.
        
        Using the model-id to do later inference:
        
        ```python
        from swift import Swift
        model = Swift.from_pretrained(model, 'my-group/some-repo-id-modelscope')
        ```
        
        Here shows a runnable example:
        
        ```python
        import os
        import tempfile
        
        # Please install modelscope by `pip install modelscope`
        from modelscope import Model
        
        from swift import LoRAConfig, SwiftModel, Swift, push_to_hub
        
        tmp_dir = tempfile.TemporaryDirectory().name
        if not os.path.exists(tmp_dir):
            os.makedirs(tmp_dir)
        
        
        model = Model.from_pretrained('modelscope/Llama-2-7b-ms', device_map='auto')
        lora_config = LoRAConfig(target_modules=['q_proj', 'k_proj', 'v_proj'])
        model: SwiftModel = Swift.prepare_model(model, lora_config)
        # Do some finetuning here
        model.save_pretrained(tmp_dir)
        
        push_to_hub('my-group/swift_llama2', output_dir=tmp_dir)
        model = Model.from_pretrained('modelscope/Llama-2-7b-ms', device_map='auto')
        model = SwiftModel.from_pretrained(model, 'my-group/swift_llama2', device_map='auto')
        ```
        
        This is a example that uses transformers for model creation uses SWIFT for efficient tuning.
        
        ```python
        from swift import Swift, LoRAConfig, AdapterConfig, PromptConfig
        from transformers import AutoModelForImageClassification
        
        # init vit model
        model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch16-224")
        
        # init lora tuner config
        lora_config = LoRAConfig(
            r=10,  # the rank of the LoRA module
            target_modules=['query', 'key', 'value'],  # the modules to be replaced with the end of the module name
            merge_weights=False  # whether to merge weights
        )
        
        # init adapter tuner config
        adapter_config = AdapterConfig(
            dim=768,  # the dimension of the hidden states
            hidden_pos=0,  # the position of the hidden state to passed into the adapter
            target_modules=r'.*attention.output.dense$',  # the modules to be replaced with regular expression
            adapter_length=10  # the length of the adapter length
        )
        
        # init prompt tuner config
        prompt_config = PromptConfig(
            dim=768,  # the dimension of the hidden states
            target_modules=r'.*layer\.\d+$',  # the modules to be replaced with regular expression
            embedding_pos=0,    # the position of the embedding tensor
            prompt_length=10,   # the length of the prompt tokens
            attach_front=False  # Whether prompt is attached in front of the embedding
        )
        
        # create model with swift. In practice, you can use any of these tuners or a combination of them.
        model = Swift.prepare_model(model, {"lora_tuner": lora_config, "adapter_tuner": adapter_config, "prompt_tuner": prompt_config})
        
        # get the trainable parameters of model
        model.get_trainable_parameters()
        # 'trainable params: 838,776 || all params: 87,406,432 || trainable%: 0.9596273189597764'
        ```
        
        You can use the features offered by Peft in SWIFT:
        
        ```python
        from swift import LoraConfig, Swift
        from peft import TaskType
        lora_config = LoraConfig(target_modules=['query', 'key', 'value'], task_type=TaskType.CAUSAL_LM)
        model_wrapped = Swift.prepare_model(model, lora_config)
        
        # or call from_pretrained to load weights in the modelhub
        model_wrapped = Swift.from_pretrained(model, 'some-id-in-the-modelscope-modelhub')
        ```
        
        
        The saving strategy between Swift tuners and Peft tuners are slightly different. You can name a tuner by:
        
        ```python
        model = Swift.prepare_model(model, {'default': LoRAConfig(...)})
        model.save_pretrained('./output')
        ```
        
        In the output dir, you will have a dir structure like this:
        
        ```text
        output
            |-- default
                |-- adapter_config.json
                |-- adapter_model.bin
            |-- adapter_config.json
            |-- adapter_model.bin
        ```
        
        The config/weights stored in the output dir is the config of `extra_state_keys` and the weights of it. This is different from PEFT, which stores the weights and config of the `default` tuner.
        
        
        # Learn More
        
        - [ModelScope library](https://github.com/modelscope/modelscope/)
        
          ModelScope Library is the model library of ModelScope project, which contains a large number of popular models.
        
        - [Contribute your own model to ModelScope](https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88)
        
        # License
        
        This project is licensed under the [Apache License (Version 2.0)](https://github.com/modelscope/modelscope/blob/master/LICENSE).
        
Keywords: python,petl,efficient tuners
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
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Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
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