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--- |
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license: apache-2.0 |
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--- |
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# X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models |
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X-LoRA works by learning scaling values for LoRA adapters. These learned scalings values are used to |
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gate the LoRA experts in a dense fashion. Additionally, all LoRA adapters and the base model are frozen, allowing efficient fine tuning due to a low parameter count. |
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X-LoRA is easily applied to any HuggingFace Transformers model. |
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## Features |
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- Effective: Dense gating of experts allows effective mixing |
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- Efficient fine-tuning: low trainable parameter count |
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- Hierarchical encapsulated strategy: Re-use existing trained models or model section and re-use them to address complex tasks that cut across experts, following a bio-inspired strategy |
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- Easy-to-use API: `add_xlora_to_model`, broad compatibility |
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- Dynamically mix LoRA adapters: Deep layer-wise combinations of adapters. |
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## X-LoRA source code |
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Install directly from source |
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``` |
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pip install git+https://github.com/EricLBuehler/xlora.git -U |
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``` |
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Further details on installation, packages with source code, API details and more examples: |
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[https://github.com/EricLBuehler/xlora](https://github.com/EricLBuehler/xlora) |
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## Converting and loading a model |
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Example for model conversation: |
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```python |
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import torch |
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import xlora |
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from transformers import AutoConfig, AutoModelForCausalLM # type: ignore |
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model = AutoModelForCausalLM.from_pretrained( |
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"mistralai/Mistral-7B-Instruct-v0.1", |
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trust_remote_code=True, |
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use_flash_attention_2=False, |
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device_map="cuda:0", |
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torch_dtype=torch.bfloat16, |
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) |
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config = AutoConfig.from_pretrained( |
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"mistralai/Mistral-7B-Instruct-v0.1", |
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trust_remote_code=True, |
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use_flash_attention_2=False, |
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device_map="auto", |
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) |
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### Convert the model to X-LoRA |
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model_created = xlora.add_xlora_to_model( |
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model=model, |
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xlora_config=xlora.xLoRAConfig(config.hidden_size, xlora_depth=8, device=torch.device("cuda")), |
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verbose=True, |
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adapters={ |
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"adapter_1": "./path/to/the/checkpoint_adapter_1/", |
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"adapter_2": "./path/to/the/checkpoint_adapter_2/", |
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"adapter_n": "./path/to/the/checkpoint_adapter_3/", |
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}, |
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) |
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``` |
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## Loading a trained X-LoRA model from scratch |
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```python |
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import torch |
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import xlora |
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from transformers import AutoConfig, AutoModelForCausalLM # type: ignore |
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model = AutoModelForCausalLM.from_pretrained( |
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"mistralai/Mistral-7B-Instruct-v0.1", |
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trust_remote_code=True, |
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use_flash_attention_2=False, |
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device_map="cuda:0", |
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torch_dtype=torch.bfloat16, |
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) |
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config = AutoConfig.from_pretrained( |
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"mistralai/Mistral-7B-Instruct-v0.1", |
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trust_remote_code=True, |
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use_flash_attention_2=False, |
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device_map="auto", |
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) |
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model = xlora.from_pretrained( |
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"./path/to/saved/model", |
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model, |
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{ |
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"adapter_1": "./path/to/the/checkpoint/", |
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"adapter_2": "./path/to/the/checkpoint/", |
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"adapter_n": "./path/to/the/checkpoint/", |
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}, |
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"cuda", |
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) |
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``` |
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## Loading pre-trained X-LoRA model directly from Hugging Face Hub |
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```python |
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import torch |
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from xlora.xlora_utils import load_model |
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XLoRa_model_name = 'lamm-mit/x-lora' |
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model,tokenizer=load_model(model_name = XLoRa_model_name, |
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device='cuda:0', |
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use_flash_attention_2=True, |
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dtype=torch.bfloat16, |
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) |
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) |
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``` |
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Inference: |
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```python |
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def generate_response (model, tokenizer, |
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text_input="What is the best biomaterial for superior strength?", |
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num_return_sequences = 1, |
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temperature = 0.75, |
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max_new_tokens = 127, |
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num_beams = 1, |
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top_k = 50, |
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top_p = 0.9, |
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repetition_penalty=1., |
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eos_token_id=2, |
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add_special_tokens=True, |
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): |
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inputs = tokenizer(text_input, add_special_tokens=add_special_tokens) |
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with torch.no_grad(): |
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outputs = model.generate(input_ids = inputs["input_ids"], |
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attention_mask = inputs["attention_mask"] , |
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max_new_tokens=max_new_tokens, |
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temperature=temperature, |
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num_beams=num_beams, |
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top_k = top_k, |
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top_p = top_p, |
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num_return_sequences = num_return_sequences, |
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eos_token_id=eos_token_id, |
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pad_token_id = eos_token_id, |
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do_sample =True, |
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repetition_penalty=repetition_penalty, |
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) |
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return tokenizer.batch_decode(outputs[:,inputs["input_ids"].shape[1]:].detach().cpu().numpy(), skip_special_tokens=True) |
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output_text=generate_response (model, tokenizer, text_input=txt,eos_token_id=eos_token, |
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num_return_sequences=1, repetition_penalty=1.1, |
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top_p=0.9, top_k=512, |
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temperature=0.5, |
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max_new_tokens=256) |
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print (output_text[0]) |
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``` |
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## Dataset |
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See [lamm-mit/x-lora-dataset](https://huggingface.co/datasets/lamm-mit/x-lora-dataset) for the dataset used to train the X-LoRA model. Details on the datasets used to train the original adapters are included in the paper (see reference below). |
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## Sample results |
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## Acknowledgements |
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This work is built on the Hugging Face [PEFT library](https://github.com/huggingface/peft/tree/main/) and other components in the Hugging Face ecosystem. We acknowledge the authors of this excellent library and related methods. |
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## Original paper and citation |
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Cite this work as: |
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```bibtex |
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@article{Buehler_XLoRA_2024, |
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title = {X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models with Applications in Protein Mechanics and Design}, |
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author = {E.L. Buehler, M.J. Buehler}, |
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journal = {}, |
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year = {2024}, |
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volume = {}, |
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pages = {}, |
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url = {https://arxiv.org/abs/2402.07148} |
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} |
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``` |
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