Nanbeige 16B Chat 32K GGUF
The Nanbeige 16B Chat 32K model is a remarkable AI model that's efficient and fast. It's a 16 billion parameter language model developed by Nanbeige LLM Lab, trained on 2.5T Tokens, and fine-tuned for chat and conversation. With its unique architecture and training data, it can handle tasks like text generation, conversation, and more with ease. What sets it apart is its ability to provide high-quality responses while keeping costs down, making it a practical choice for users. Whether you're looking for a model for chat or other tasks, the Nanbeige 16B Chat 32K is worth considering.
Table of Contents
Model Overview
The Nanbeige 16B Chat 32K model is a powerful language model developed by Nanbeige LLM Lab. It has 16B parameters and was trained on a massive dataset of 2.5T Tokens, including high-quality internet corpus, books, and code.
Capabilities
This model is designed to engage in natural-sounding conversations, answering questions and responding to prompts in a helpful and informative way. It can also generate text on a wide range of topics, from short answers to longer passages and even entire articles. Additionally, it can generate code in various programming languages, making it a useful tool for developers and programmers.
Some of its key features include:
- Large parameter count: With
16Bparameters, this model is capable of learning complex patterns in language. - Extended context length: The model can handle input sequences of up to
32Ktokens, making it suitable for tasks that require processing long texts. - Human-aligned training: The model has undergone extensive human-aligned training, enabling it to respond more accurately and safely to user queries.
Quantization Methods
The model is available in various quantization formats, including:
| Quantization Method | Bits | Size | Max RAM Required |
|---|---|---|---|
| Q2_K | 2 | 6.64 GB | 9.14 GB |
| Q3_K_S | 3 | 6.93 GB | 9.43 GB |
| Q3_K_M | 3 | 7.74 GB | 10.24 GB |
| Q3_K_L | 3 | 8.45 GB | 10.95 GB |
| Q4_0 | 4 | 8.99 GB | 11.49 GB |
| Q4_K_S | 4 | 9.04 GB | 11.54 GB |
| Q4_K_M | 4 | 9.59 GB | 12.09 GB |
| Q5_0 | 5 | 10.93 GB | 13.43 GB |
| Q5_K_S | 5 | 10.93 GB | 13.43 GB |
| Q5_K_M | 5 | 11.24 GB | 13.74 GB |
| Q6_K | 6 | 12.99 GB | 15.49 GB |
| Q8_0 | 8 | 16.83 GB | 19.33 GB |
Performance
The model’s speed is influenced by the quantization method used. With the Q4_K_M method, it achieves a balanced quality and speed. However, if you prioritize speed over quality, you can opt for the Q2_K method, which results in a smaller file size and faster processing time.
| Quantization Method | File Size | Max RAM Required |
|---|---|---|
| Q2_K | 6.64 GB | 9.14 GB |
| Q4_K_M | 9.59 GB | 12.09 GB |
Example Use Cases
- Customer service chatbots: The model can be used to power customer service chatbots that provide helpful and informative responses to customer inquiries.
- Content generation: The model can be used to generate high-quality content, such as articles and blog posts, on a wide range of topics.
- Code completion: The model can be used to complete code snippets and provide suggestions for developers and programmers.
Limitations
While this model is powerful, it’s not perfect. It has some limitations, including:
- Limited context understanding: The model can process up to
32Ktokens, but it may struggle to understand the context of very long texts or conversations. - Lack of common sense: The model is not perfect and can make mistakes. It may not always understand the nuances of human language or the context of a situation.
- Limited domain knowledge: The model has been trained on a vast amount of text data, but it’s not a specialist in any particular domain. It may not have the same level of knowledge or expertise as a human expert in a particular field.
Format
The model uses a specific format called GGUF (Generative Gradient Update Format). This format is designed to be more efficient and flexible than previous formats.
Architecture
The model is based on a transformer architecture, which is a type of neural network that is well-suited for natural language processing tasks. The model has 16B parameters and is trained on a large dataset of text.
Data Formats
The model supports several data formats, including:
- Text: The model accepts input in the form of text sequences, which can be tokenized and pre-processed before being fed into the model.
- GGUF: The model is stored in the GGUF format, which is a binary format that contains the model’s weights and other metadata.
Special Requirements
The model has several special requirements for input and output:
- Input: The input text should be tokenized and pre-processed before being fed into the model. This can be done using a library such as
ctransformers. - Output: The output of the model is a probability distribution over the vocabulary, which can be used to generate text.
Example Code
Here is an example of how to use the model in Python:
from ctransformers import AutoModelForCausalLM
# Load the model
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Nanbeige-16B-Chat-32K-GGUF", model_file="nanbeige-16b-chat-32k.Q4_K_M.gguf", model_type="nanbeige")
# Generate text
output = llm("Hello, how are you?")
print(output)
