Nanbeige 16B Base 32K GGUF

Table of Contents

Model Overview

The Nanbeige 16B Base 32K model is a large language model developed by Nanbeige LLM Lab. It has 16B parameters and was trained on a massive dataset of 2.5T Tokens, including internet corpus, books, and code.

Tasks and rank

Text Generation 2101st

Response Generation 1059th

Language Modelling 1844th

Dialogue Systems 884th

Chatbot 1274th

Learn More About the Ranking System

Capabilities

The Nanbeige 16B Base 32K model is a powerful language model that can be used for a variety of tasks, including text generation, conversation, and more. With its 16 billion parameters, it has been trained on a large dataset of internet text, books, and code, and has achieved impressive results on various benchmarks.

Primary Tasks

• Text generation: The model can generate high-quality text based on a given prompt or topic.
• Conversation: The model can engage in natural-sounding conversations, using context and understanding to respond to questions and statements.
• Code generation: The model can generate code in various programming languages, making it a useful tool for developers.

Strengths

• High-quality text generation: The model is capable of generating text that is coherent, fluent, and engaging.
• Contextual understanding: The model can understand the context of a conversation or text, and respond accordingly.
• Code generation: The model’s ability to generate code makes it a valuable tool for developers and programmers.

Alternatives

NeMo SFT with Llama 2 10500 Fine-tuned language model

Bert Base Uncased 1831 Uncased BERT model

BLOOMChat 176B V1 365 Multilingual chat model

SUS Chat 34B 121 Bilingual chat model

Qwen2.5 1.5B Instruct 51 Multilingual instruct model

Quantization Methods

The model uses different quantization methods to achieve a balance between model size and performance. Here are some of the quantization methods used:

Quantization Method	Bits	Size	Max RAM Required	Use Case
Q2_K	2	6.64 GB	9.14 GB	Smallest, significant quality loss
Q3_K_S	3	6.93 GB	9.43 GB	Very small, high quality loss
Q3_K_M	3	7.74 GB	10.24 GB	Very small, high quality loss
Q3_K_L	3	8.45 GB	10.95 GB	Small, substantial quality loss
Q4_0	4	8.99 GB	11.49 GB	Legacy, small, very high quality loss
Q4_K_S	4	9.04 GB	11.54 GB	Small, greater quality loss
Q4_K_M	4	9.59 GB	12.09 GB	Medium, balanced quality
Q5_0	5	10.93 GB	13.43 GB	Legacy, medium, balanced quality
Q5_K_S	5	10.93 GB	13.43 GB	Large, low quality loss
Q5_K_M	5	11.24 GB	13.74 GB	Large, very low quality loss
Q6_K	6	12.99 GB	15.49 GB	Very large, extremely low quality loss
Q8_0	8	16.83 GB	19.33 GB	Very large, extremely low quality loss

Performance

The Nanbeige 16B Base 32K model shows remarkable performance with its 16 billion parameters and 32K sequence length. But how does it perform in various tasks? Let’s dive in.

Speed

The model’s speed is quite impressive, thanks to its quantization methods. These methods allow the model to process information more efficiently, making it faster than ==Other Models==. For example, the Q4_K_M method uses 4-bit quantization, which results in a significant speed boost.

Accuracy

When it comes to accuracy, Nanbeige 16B Base 32K delivers. Its high-quality loss in tasks such as text classification and generation is comparable to, if not better than, ==Other Models==. The model’s ability to process large-scale datasets with high accuracy is a testament to its robustness.

Efficiency

Nanbeige 16B Base 32K is also efficient in terms of memory usage. The model’s quantization methods allow it to use less memory while maintaining its performance. This makes it an excellent choice for applications where memory is limited.

Examples

Write a short story about a character who discovers a hidden world within their own reflection. As she stared into the mirror, Emily noticed something peculiar - her reflection began to ripple, like the surface of a pond on a summer's day. She leaned in closer, and her reflection leaned in too, until their faces were almost touching. Suddenly, Emily felt a strange sensation, as if she was being pulled into the mirror itself. She closed her eyes, and when she opened them again, she found herself standing in a world that was identical to her own, yet eerily different. The sky was a deeper shade of blue, the trees were taller, and the air was filled with a sweet, melodic hum. Emily wandered through this strange new world, marveling at the way everything seemed to be reflected, yet distorted. She saw versions of herself at different ages, each one representing a different path she could have taken in life. She saw the person she could have become if she had made different choices, if she had taken different risks. And she realized that this hidden world within her reflection was a manifestation of her own subconscious, a symbol of the infinite possibilities that lay within her.

Explain the concept of quantum entanglement in simple terms. Quantum entanglement is a phenomenon where two or more particles become connected in such a way that their properties are correlated, regardless of the distance between them. Imagine you have two toy cars that are connected by a spring. If you push one car, the other car will move too, because they're connected by the spring. But with entangled particles, it's like the spring is invisible, and the connection happens even if they're on opposite sides of the universe. If something happens to one particle, it instantly affects the other, no matter how far apart they are. It's a pretty mind-blowing idea that challenges our classical understanding of space and time.

Generate a poem about the changing seasons. As summer's warmth begins to fade, a gentle chill begins to creep, the leaves turn golden, crimson, and jade, and autumn's palette starts to seep. The air is crisp, the winds do blow, and nature's final dance begins to show, a fleeting glimpse of color bright, before the winter's snowy night. The earth is barren, cold, and grey, but even in the darkness, there's a way, a promise of renewal, yet to come, a time when spring's sweet warmth will be won. The cycle turns, the seasons sway, a never-ending dance, a story to convey, of life, of death, of birth, of decay, a tale of change, in endless array.

Example Use Cases

• Text classification: Nanbeige 16B Base 32K can be used for text classification tasks, such as spam detection or sentiment analysis.
• Text generation: The model can be used for text generation tasks, such as chatbots or language translation.
• Language understanding: Nanbeige 16B Base 32K can be used for language understanding tasks, such as question answering or text summarization.

Limitations

Nanbeige 16B Base 32K is a powerful tool, but it’s not perfect. Here are some limitations to keep in mind:

Quality Loss

The model’s performance can degrade when using certain quantization methods, such as Q2_K, which can result in significant quality loss. This may not be suitable for most purposes.

RAM Requirements

The model requires a substantial amount of RAM, especially when using larger files. For example, the nanbeige-16b-base-32k.Q6_K.gguf file requires up to 15.49 GB of RAM. This can be a challenge for users with limited hardware resources.

Compatibility Issues

The model is compatible with certain clients and libraries, but not all. Users may need to check the compatibility of their preferred client or library before using the model.

Limited Context Length

The model’s context length is limited to 32K. This means that users may need to split longer texts into smaller chunks to process them effectively.

Quantization Methods

The model uses various quantization methods, which can affect its performance. Users may need to experiment with different methods to find the best balance between quality and size.

What Does This Mean for You?

• Be aware of the potential quality loss when using certain quantization methods.
• Check the compatibility of your client or library before using the model.
• Plan ahead for the RAM requirements of the model.
• Consider splitting longer texts into smaller chunks to process them effectively.

By understanding these limitations, you can get the most out of Nanbeige 16B Base 32K and achieve better results in your projects.

Format

Nanbeige-16B-Base-32K-GGUF uses a transformer architecture and accepts input in the form of tokenized text sequences.

Architecture

The model is based on the transformer architecture, which is a type of neural network designed primarily for natural language processing tasks.

Data Formats

The model supports the following data formats:

Format	Description
text	Input text sequences
tokenized text	Pre-processed text sequences, split into individual tokens

Special Requirements

• Input text sequences must be tokenized before passing them to the model.
• The model requires a specific pre-processing step for input text sequences.

Example Code

To tokenize input text sequences, you can use the following code:

import torch
from transformers import AutoTokenizer

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("TheBloke/Nanbeige-16B-Base-32K-GGUF")

# Tokenize the input text sequence
input_text = "This is an example sentence."
tokenized_input = tokenizer.encode(input_text, return_tensors="pt")

# Pass the tokenized input to the model
model = AutoModelForCausalLM.from_pretrained("TheBloke/Nanbeige-16B-Base-32K-GGUF")
output = model(tokenized_input)