BiLLM 0.1.3

Tool for converting LLMs from uni-directional to bi-directional for tasks like classification and sentence embeddings.

BiLLM

Tool for converting LLMs from uni-directional to bi-directional for tasks like classification and sentence embeddings. Compatible with 🤗 transformers.

Usage

1. python -m pip install -U billm

2. Specify start index for bi-directional layers via export BiLLM_START_INDEX={layer_index}. if not specified, default is 0, i.e., all layers are bi-directional. If set to -1, BiLLM is disabled.

3. Import LLMs from BiLLM and initialize them as usual with transformers.

- from transformers import (
-    LLamaModel,
-    LLamaForCausalLM,
-    LLamaForSequenceClassification,
-    MistralModel,
-    MistralForCausalLM,
-    MistralForSequenceClassification
-    Qwen2Model,
-    Qwen2ForCausalLM,
-    Qwen2ForSequenceClassification
- )

+ from billm import (
+    LLamaModel,
+    LLamaForCausalLM,
+    LLamaForSequenceClassification,
+    LLamaForTokenClassification,
+    MistralModel,
+    MistralForCausalLM,
+    MistralForSequenceClassification,
+    MistralForTokenClassification,
+    Qwen2Model,
+    Qwen2ForCausalLM,
+    Qwen2ForSequenceClassification,
+    Qwen2ForTokenClassification
+ )

Examples

NER

training:

$ cd examples
$ WANDB_MODE=disabled BiLLM_START_INDEX=0 CUDA_VISIBLE_DEVICES=3 python billm_ner.py \
--model_name_or_path mistralai/Mistral-7B-v0.1 \
--dataset_name_or_path conll2003 \
--push_to_hub 0

inference:

from transformers import AutoTokenizer, pipeline
from peft import PeftModel, PeftConfig
from billm import MistralForTokenClassification


label2id = {'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8}
id2label = {v: k for k, v in label2id.items()}
model_id = 'WhereIsAI/billm-mistral-7b-conll03-ner'
tokenizer = AutoTokenizer.from_pretrained(model_id)
peft_config = PeftConfig.from_pretrained(model_id)
model = MistralForTokenClassification.from_pretrained(
    peft_config.base_model_name_or_path,
    num_labels=len(label2id), id2label=id2label, label2id=label2id
)
model = PeftModel.from_pretrained(model, model_id)
# merge and unload is necessary for inference
model = model.merge_and_unload()

token_classifier = pipeline("token-classification", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
sentence = "I live in Hong Kong. I am a student at Hong Kong PolyU."
tokens = token_classifier(sentence)
print(tokens)

Supported Models

• LLaMA
• Mistral

Citation

If you use this toolkit in your work, please cite the following paper:

1. For sentence embeddings modeling:

@inproceedings{li2024bellm,
    title = "BeLLM: Backward Dependency Enhanced Large Language Model for Sentence Embeddings",
    author = "Li, Xianming and Li, Jing",
    booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics",
    year = "2024",
    publisher = "Association for Computational Linguistics"
}

2. For other tasks:

@article{li2023label,
  title={Label supervised llama finetuning},
  author={Li, Zongxi and Li, Xianming and Liu, Yuzhang and Xie, Haoran and Li, Jing and Wang, Fu-lee and Li, Qing and Zhong, Xiaoqin},
  journal={arXiv preprint arXiv:2310.01208},
  year={2023}
}