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---
license: apache-2.0
---
# X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models
X-LoRA works by learning scaling values for LoRA adapters. These learned scalings values are used to
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.
X-LoRA is easily applied to any HuggingFace Transformers model.
## Features
- Effective: Dense gating of experts allows effective mixing
- Efficient fine-tuning: low trainable parameter count
- 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
- Easy-to-use API: `add_xlora_to_model`, broad compatibility
- Dynamically mix LoRA adapters: Deep layer-wise combinations of adapters.
## X-LoRA source code
Install directly from source
```
pip install git+https://github.com/EricLBuehler/xlora.git -U
```
Further details on installation, packages with source code, API details and more examples:
[https://github.com/EricLBuehler/xlora](https://github.com/EricLBuehler/xlora)
## Converting and loading a model
Example for model conversation:
```python
import torch
import xlora
from transformers import AutoConfig, AutoModelForCausalLM # type: ignore
model = AutoModelForCausalLM.from_pretrained(
"mistralai/Mistral-7B-Instruct-v0.1",
trust_remote_code=True,
use_flash_attention_2=False,
device_map="cuda:0",
torch_dtype=torch.bfloat16,
)
config = AutoConfig.from_pretrained(
"mistralai/Mistral-7B-Instruct-v0.1",
trust_remote_code=True,
use_flash_attention_2=False,
device_map="auto",
)
### Convert the model to X-LoRA
model_created = xlora.add_xlora_to_model(
model=model,
xlora_config=xlora.xLoRAConfig(config.hidden_size, xlora_depth=8, device=torch.device("cuda")),
verbose=True,
adapters={
"adapter_1": "./path/to/the/checkpoint_adapter_1/",
"adapter_2": "./path/to/the/checkpoint_adapter_2/",
"adapter_n": "./path/to/the/checkpoint_adapter_3/",
},
)
```
## Loading a trained X-LoRA model from scratch
```python
import torch
import xlora
from transformers import AutoConfig, AutoModelForCausalLM # type: ignore
model = AutoModelForCausalLM.from_pretrained(
"mistralai/Mistral-7B-Instruct-v0.1",
trust_remote_code=True,
use_flash_attention_2=False,
device_map="cuda:0",
torch_dtype=torch.bfloat16,
)
config = AutoConfig.from_pretrained(
"mistralai/Mistral-7B-Instruct-v0.1",
trust_remote_code=True,
use_flash_attention_2=False,
device_map="auto",
)
model = xlora.from_pretrained(
"./path/to/saved/model",
model,
{
"adapter_1": "./path/to/the/checkpoint/",
"adapter_2": "./path/to/the/checkpoint/",
"adapter_n": "./path/to/the/checkpoint/",
},
"cuda",
)
```
## Loading pre-trained X-LoRA model
```python
import torch
from xlora.xlora_utils import load_model # type: ignore
XLoRA_model_name = "lamm-mit/x-lora/X-LoRA"
model, tokenizer = load_model(
model_name="HuggingFaceH4/zephyr-7b-beta",
device="cuda:0",
dtype=torch.bfloat16,
fine_tune_model_name=XLoRA_model_name,
adapters={
"adapter_1": "lamm-mit/x-lora/X-LoRA_adapters/1/",
"adapter_2": "lamm-mit/x-lora/X-LoRA_adapters/2/",
"adapter_3": "lamm-mit/x-lora/X-LoRA_adapters/3/",
"adapter_4": "lamm-mit/x-lora/X-LoRA_adapters/4/",
"adapter_5": "lamm-mit/x-lora/X-LoRA_adapters/5/",
"adapter_6": "lamm-mit/x-lora/X-LoRA_adapters/6/",
"adapter_7": "lamm-mit/x-lora/X-LoRA_adapters/7/",
"adapter_8": "lamm-mit/x-lora/X-LoRA_adapters/8/",
"adapter_9": "lamm-mit/x-lora/X-LoRA_adapters/9/",
},
)
```
Inference:
```python
def generate_response (model, tokenizer,
text_input="What is the best biomaterial for superior strength?",
num_return_sequences = 1,
temperature = 0.75,
max_new_tokens = 127,
num_beams = 1,
top_k = 50,
top_p = 0.9,
repetition_penalty=1.,
eos_token_id=2,
add_special_tokens=True,
):
inputs = tokenizer(text_input, add_special_tokens=add_special_tokens)
with torch.no_grad():
outputs = model.generate(input_ids = inputs["input_ids"],
attention_mask = inputs["attention_mask"] ,
max_new_tokens=max_new_tokens,
temperature=temperature,
num_beams=num_beams,
top_k = top_k,
top_p = top_p,
num_return_sequences = num_return_sequences,
eos_token_id=eos_token_id,
pad_token_id = eos_token_id,
do_sample =True,
repetition_penalty=repetition_penalty,
)
return tokenizer.batch_decode(outputs[:,inputs["input_ids"].shape[1]:].detach().cpu().numpy(), skip_special_tokens=True)
output_text=generate_response (model, tokenizer, text_input=txt,eos_token_id=eos_token,
num_return_sequences=1, repetition_penalty=1.1,
top_p=0.9, top_k=512,
temperature=0.5,
max_new_tokens=256)
print (output_text[0])
```
## Dataset
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).
## Acknowledgements
This work is built on the Hugging Face [PEFT library](https://github.com/huggingface/peft/tree/main/src/peft) and other components in the Hugging Face ecosystem.
## Original paper and citation
Cite this work as:
```bibtex
@article{Buehler_XLoRA_2024,
title = {X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models with Applications in Protein Mechanics and Design},
author = {E.L. Buehler, M.J. Buehler},
journal = {},
year = {2024},
volume = {},
pages = {},
url = {https://arxiv.org/abs/XXXX.YYYYY}
}
```
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