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Model: knowledgator/Qwen-encoder-0.5B
Source: Original Platform

.gitattributes

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README.md

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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 Qwen2-0.5B 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 Qwen2BiModel
+
+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/Qwen-encoder-0.5B"
+)
+
+model = Qwen2BiModel.from_pretrained("knowledgator/Qwen-encoder-0.5B")
+
+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/Qwen-encoder-0.5B')
+question_answering_model = AutoLLMEncoderForQuestionAnswering.from_pretrained('knowledgator/Qwen-encoder-0.5B')
+token_classification_model = AutoLLMEncoderForTokenClassification.from_pretrained('knowledgator/Qwen-encoder-0.5B')
+```
+
+### 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/Qwen-encoder-0.5B')
+
+# 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).
+

added_tokens.json

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+{
+  "<|endoftext|>": 151643,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644
+}

config.json

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+{
+  "_name_or_path": "/home/BiomikeeNew/werent4/llm2vec/output/mntp/qwen_0.5/checkpoint-120000",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 896,
+  "initializer_range": 0.02,
+  "intermediate_size": 4864,
+  "max_position_embeddings": 131072,
+  "max_window_layers": 24,
+  "model_type": "qwen2",
+  "num_attention_heads": 14,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 1000000.0,
+  "sliding_window": 131072,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.2",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "transformers_version": "4.40.2"
+}

model.safetensors

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+size 1976163472

special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>"
+  ],
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": "_",
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "62": {
+      "content": "_",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "mask_token": "_",
+  "model_max_length": 32768,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}