Nanbeige2 8B Chat
The Nanbeige2 8B Chat model is a powerful tool that leverages 4.5 trillion tokens of high-quality training data to deliver superior performance in various tasks, such as question-answering and conversation. Developed through a rigorous process involving Supervised Fine-Tuning, curriculum learning, and human feedback, this model has achieved notable results on benchmark datasets like AlpacaEval 2.0, AlignBench, and MT-Bench. But what really sets it apart is its ability to balance performance with efficiency, making it a practical choice for real-world applications. However, as with any AI model, there's a risk of generating unexpected outputs, so it's essential to use it responsibly and in compliance with the Apache 2.0 License and the License Agreement for Large Language Models Nanbeige.
Table of Contents
Model Overview
The Nanbeige2-8B-Chat model is a state-of-the-art language model designed to understand and respond to human input in a conversational manner. It’s developed by the Nanbeige Lab and has been trained on a massive dataset of 4.5T tokens of high-quality text.
Capabilities
The model is capable of engaging in natural-sounding conversations with humans, answering questions on a wide range of topics, and understanding human language, including nuances and context.
What can it do?
- Answer questions on various subjects, from science and history to entertainment and culture
- Engage in conversations and respond to user input
- Generate text based on a given prompt or topic
- Understand and respond to multiple turns of conversation
How well does it perform?
The model has been evaluated on several benchmark datasets, including AlpacaEval 2.0, AlignBench, and MT-Bench, and has achieved impressive results.
| Benchmark | Result |
|---|---|
| AlpacaEval 2.0 | 30.1% |
| AlignBench | 7.02 |
| MT-Bench | 8.44 |
What makes it unique?
The model has been trained using a combination of supervised fine-tuning and human feedback, which allows it to learn from a wide range of sources and adapt to different conversational styles.
Performance
The model demonstrates impressive performance in terms of speed, accuracy, and efficiency. With 8B parameters, it’s capable of handling large amounts of data quickly and efficiently.
Speed
The model uses a fast tokenizer and a causal language model to generate responses quickly.
Accuracy
The model has achieved notable results on various benchmark datasets, outperforming many other models in its class.
Efficiency
The model is designed to be efficient in its use of data and can automatically map its computations to the most efficient device available.
Limitations
While the Nanbeige2-8B-Chat model is powerful, it’s not perfect. It may generate unexpected or unwanted outputs, including biased or discriminatory content.
Unexpected Outputs
The model may generate harmful content, such as bias or discrimination, due to its size and probabilistic nature.
Size and Complexity
The model is a large model with 8B parameters, which can make it resource-intensive to run and maintain.
Training Data Limitations
The model was trained on a large dataset, but this dataset is not exhaustive, and the model may not perform well on tasks or domains that are not well-represented in the training data.
Using the Model
If you’re interested in using the Nanbeige2-8B-Chat model, you can access it through the Hugging Face Transformers library.
Here’s an example of how to use the model in Python:
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('Nanbeige/Nanbeige2-8B-Chat', use_fast=False, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained('Nanbeige/Nanbeige2-8B-Chat', torch_dtype='auto', device_map='auto', trust_remote_code=True)
messages = [{'role': 'user', 'content': 'Hello'}]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
input_ids = tokenizer(prompt, add_special_tokens=False, return_tensors='pt').input_ids
output_ids = model.generate(input_ids.to('cuda'))
resp = tokenizer.decode(output_ids[0][len(input_ids[0]):], skip_special_tokens=True)
print(resp)
Note that you’ll need to comply with the Apache 2.0 License and the License Agreement for Large Language Models Nanbeige when using the Nanbeige2-8B-Chat model. If you plan to use the model for commercial purposes, you’ll need to submit an application and obtain a commercial copyright license.
