knowledgator
/

Llama-encoder-1.0B

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 ---
+license: apache-2.0
+datasets:
+- wikimedia/wikipedia
+language:
+- en
 library_name: transformers
+tags:
+- LLM2Vec
+- encoder
+- LLM
+- classification
+- NER
+- question-answering
 ---
+# LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
+> LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fine-tuned to achieve state-of-the-art performance.
+- **Repository:** https://github.com/McGill-NLP/llm2vec
+- **Paper:** https://arxiv.org/abs/2404.05961
+## Overview:
+This is a bi-directional version of Tiny-LLaMA-1.0B trained with masked token prediction on the Wikipedia dataset. Modern decoder models offer several advantages over classical encoders like BERT:
+They are pre-trained on more recent textual corpora
+They are trained on larger and more diverse datasets
+Modern decoders have better support for long-context windows
+Flash-attention support is available for these models
+Considering these benefits, we are excited to release a series of decoder models tuned to work in a bi-directional setting. This approach combines the strengths of modern decoder architectures with the versatility of bi-directional context understanding, potentially opening up new possibilities for various natural language processing tasks, such as NER.
+In comparison to original LLM2Vec we trained all weights of LLama model, it potentially improve bi-directional abilities of the model.
+## Installation
+```bash
+pip install llm2vec
+```
+## Usage
+```python
+from llm2vec.models import LlamaBiModel
+import torch
+from transformers import AutoTokenizer
+# Loading base Mistral model, along with custom code that enables bidirectional connections in decoder-only LLMs. MNTP LoRA weights are merged into the base model.
+tokenizer = AutoTokenizer.from_pretrained(
+    "knowledgator/Llama-encoder-1.0B"
+)
+model = LLamaBiModel.from_pretrained("knowledgator/Llama-encoder-1.0B")
+text = "The quick brown fox jumps over the lazy dog."
+inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+inputs = {k: v.to(device) for k, v in inputs.items()}
+with torch.no_grad():
+    outputs = model(**inputs)
+last_hidden_states = outputs.last_hidden_state
+```
+Here's an improved and expanded version of the README snippet:
+## Adapting for Different Discriminative Tasks
+Our bi-directional LLaMA model can be easily adapted for various discriminative tasks such as text classification, question answering, and token classification.
+To use these specialized versions, we provide a [fork of LLM2Vec](https://github.com/Knowledgator/llm2vec) with additional functionality.
+### Installation
+To get started, clone our fork of LLM2Vec and install it:
+```bash
+git clone https://github.com/Knowledgator/llm2vec.git
+cd llm2vec
+pip install -e .
+```
+Using `-e` flag installs the package in editable mode, which is useful for development.
+### Usage
+Here's how to import and use the models for different tasks:
+```python
+from llm2vec import (
+    AutoLLMEncoderForSequenceClassification,
+    AutoLLMEncoderForQuestionAnswering,
+    AutoLLMEncoderForTokenClassification
+)
+# Load models for different tasks
+classification_model = AutoLLMEncoderForSequenceClassification.from_pretrained('knowledgator/Llama-encoder-1.0B')
+question_answering_model = AutoLLMEncoderForQuestionAnswering.from_pretrained('knowledgator/Llama-encoder-1.0B')
+token_classification_model = AutoLLMEncoderForTokenClassification.from_pretrained('knowledgator/Llama-encoder-1.0B')
+```
+### Example: Text Classification
+Here's a basic example of how to use the model for text classification:
+```python
+from transformers import AutoTokenizer
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained('knowledgator/Llama-encoder-1.0B')
+# Prepare input
+text = "This movie is great!"
+inputs = tokenizer(text, return_tensors="pt")
+# Get classification logits
+outputs = classification_model(**inputs)
+logits = outputs.logits
+# The logits can be used with a softmax function to get probabilities
+# or you can use torch.argmax(logits, dim=1) to get the predicted class
+```
+### Fine-tuning
+To fine-tune these models on your specific task:
+1. Prepare your dataset in a format compatible with HuggingFace's `datasets` library.
+2. Use the `Trainer` class from HuggingFace's `transformers` library to fine-tune the model.
+Here's a basic example:
+```python
+from transformers import Trainer, TrainingArguments
+from datasets import load_dataset
+# Load your dataset
+dataset = load_dataset("your_dataset")
+# Define training arguments
+training_args = TrainingArguments(
+    output_dir="./results",
+    num_train_epochs=3,
+    per_device_train_batch_size=8,
+    per_device_eval_batch_size=8,
+    warmup_steps=500,
+    weight_decay=0.01,
+    logging_dir="./logs",
+)
+# Initialize Trainer
+trainer = Trainer(
+    model=classification_model,
+    args=training_args,
+    train_dataset=dataset["train"],
+    eval_dataset=dataset["test"],
+)
+# Fine-tune the model
+trainer.train()
+```
+### Contributing
+We welcome contributions! If you have suggestions for improvements or encounter any issues, please open an issue or submit a pull request on our [GitHub repository](https://github.com/Knowledgator/llm2vec).