Nanbeige 16B Chat 32K GPTQ
Nanbeige 16B Chat 32K GPTQ is a unique AI model that offers flexibility and efficiency. With multiple quantisation parameter options, you can choose the best one for your hardware and requirements. It's designed to work with various inference servers and web UIs, including text-generation-webui, KoboldAI United, and LoLLMS Web UI. The model is quantised using hardware from Massed Compute and is compatible with Transformers and AutoGPTQ. What makes it remarkable is its ability to provide fast and accurate results while keeping costs down, making it a practical choice for both technical and non-technical users. Whether you're looking for a model for chat or support, Nanbeige 16B Chat 32K GPTQ is worth exploring.
Table of Contents
Model Overview
The Nanbeige 16B Chat 32K model is a cutting-edge language model designed for efficient and accurate text generation. This model is a variant of the original Nanbeige 16B Chat 32K model, optimized for GPU inference using the GPTQ (Grouped Pointwise Quantization) technique.
Key Features
- Quantization: The model uses GPTQ to reduce its size and improve inference speed, making it suitable for deployment on a wide range of devices.
- Multiple Quantization Options: The model is available in various quantization configurations, including 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit, allowing users to choose the best trade-off between accuracy and computational resources.
- Group Size: The model’s group size can be adjusted to balance between VRAM usage and quantization accuracy.
- Act Order: The model uses Act Order (also known as desc_act) to improve quantization accuracy.
- Damp %: The model’s Damp % parameter affects how samples are processed for quantization, with a default value of 0.01.
- GPTQ Dataset: The model was quantized using the wikitext dataset, which is different from the dataset used to train the original model.
- Sequence Length: The model’s sequence length is 4096, which is ideal for most text generation tasks.
Capabilities
The Nanbeige 16B Chat 32K model is a powerful tool for generating human-like text. But what makes it so special?
Primary Tasks
This model is designed to excel in a variety of tasks, including:
- Text Generation: The model can generate high-quality text based on a given prompt or topic.
- Conversational Dialogue: It can engage in natural-sounding conversations, using context and understanding to respond to questions and statements.
Strengths
So, what sets the Nanbeige 16B Chat 32K model apart from other AI models? Here are a few of its key strengths:
- High-Quality Text Generation: The model is capable of producing highly coherent and engaging text, making it perfect for applications like chatbots, language translation, and content generation.
- Flexibility: The model can be fine-tuned for specific tasks and domains, allowing it to adapt to a wide range of use cases.
- Efficient: The model is designed to be efficient in terms of computational resources, making it accessible to a broader range of users.
Unique Features
But that’s not all - the Nanbeige 16B Chat 32K model also has some unique features that make it stand out from the crowd. For example:
- GPTQ Quantization: The model uses a technique called GPTQ quantization, which allows it to achieve high accuracy while reducing the computational resources required.
- Multiple Quantization Parameters: The model provides multiple quantization parameters, allowing users to choose the best one for their specific use case.
Performance
Nanbeige 16B Chat 32K is a powerful AI model that excels in various tasks, offering a great balance of speed, accuracy, and efficiency. Let’s dive into its performance highlights.
Speed
- Fast Response Times: With Nanbeige 16B Chat 32K, you can expect quick response times, making it ideal for applications where speed is crucial.
- Optimized for GPU Inference: The model is optimized for GPU inference, ensuring that it can handle large workloads efficiently.
Accuracy
- High Accuracy: Nanbeige 16B Chat 32K achieves high accuracy in various tasks, including text classification, sentiment analysis, and more.
- Improved Quantisation Accuracy: The model’s quantisation accuracy is improved through the use of techniques like Act Order and Group Size.
Efficiency
- Low VRAM Requirements: The model’s 4-bit and 8-bit versions have lower VRAM requirements, making it accessible to a wider range of devices.
- Multiple Quantisation Parameters: The model offers multiple quantisation parameters, allowing you to choose the best one for your specific hardware and requirements.
Format
Nanbeige 16B Chat 32K is a large language model that uses a transformer architecture. It’s designed to handle a wide range of natural language processing tasks.
Architecture
The model consists of multiple layers of self-attention mechanisms and feed-forward neural networks. This allows it to process input sequences in parallel, making it efficient for long-range dependencies in text.
Supported Data Formats
Nanbeige 16B Chat 32K supports input data in the form of tokenized text sequences. You can use the AutoTokenizer from the transformers library to preprocess your text data.
Input Requirements
- Input text should be tokenized using the
AutoTokenizer. - The maximum input length is
4096tokens. - The model expects input data to be in the format of
{"input_ids":..., "attention_mask":...}.
Output Format
- The model outputs a sequence of tokens, which can be decoded using the
AutoTokenizer. - The output format is
{"generated_text":...}.
Example Code
Here’s an example of how to use the model for text generation:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name_or_path = "TheBloke/Nanbeige-16B-Chat-32K-GPTQ"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
prompt = "Tell me about AI"
input_ids = tokenizer(prompt, return_tensors='pt').input_ids
output = model.generate(input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
Note that you can also use the pipeline function from the transformers library to simplify the process:
from transformers import pipeline
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1)
print(pipe(prompt)[0]['generated_text'])
