WizardLM 2 7B GGUF
WizardLM-2 7B is a cutting-edge AI model designed to excel in complex chat, multilingual, reasoning, and agent tasks. As part of the WizardLM-2 family, it boasts impressive performance, rivaling larger models. With 7 billion parameters, it's the fastest model in its class, achieving comparable results to 10x larger models. But what sets it apart? Its unique training system, powered by AI, enables it to learn and adapt efficiently. Want to explore its capabilities? It supports multi-turn conversations and can handle tasks like writing, coding, math, and more. Plus, it's optimized for real-world use, making it a practical choice for both technical and non-technical users.
Table of Contents
Model Overview
The WizardLM-2 7B model is a cutting-edge language model that has shown impressive performance in various tasks. But what makes it special?
Key Attributes
- Multilingual: This model can understand and respond in multiple languages, making it a great tool for global communication.
- 7B Parameters: With
7Bparameters, this model has a vast capacity to learn and improve its responses. - GGUF Format: The model is available in the GGUF format, which is a new format introduced by the llama.cpp team.
Capabilities
This model is capable of performing a variety of tasks, including:
- Multilingual Support: The model can understand and respond in multiple languages, making it a great tool for users who need to communicate in different languages.
- Complex Chat: The model is designed to handle complex conversations, including multi-turn dialogues and nuanced discussions.
- Reasoning and Agent: The model has been trained on a wide range of tasks, including reasoning and agent-based tasks, making it a great tool for users who need to generate text that requires critical thinking and problem-solving skills.
- Code Generation: The model can generate code in various programming languages, making it a great tool for developers who need to write code quickly and efficiently.
How to Use
The model can be used in various ways, including:
- Text-Generation-WebUI: A web-based interface for generating text using the model.
- Llama-CPP-Python: A Python library for using the model in your own applications.
- Command Line: The model can be used from the command line, allowing for more control and customization.
Performance
This model is a powerhouse of performance, boasting impressive speed, accuracy, and efficiency. Let’s dive into its speed, accuracy, and efficiency.
Speed
The model is incredibly fast, making it perfect for applications where time is of the essence. With its ability to process large amounts of data quickly, it’s an excellent choice for tasks that require rapid response times.
Accuracy
But speed isn’t everything - accuracy is also crucial. The model delivers on this front, demonstrating high accuracy in various tasks such as:
- Multilingual understanding
- Reasoning and problem-solving
- Complex chat and conversation
Efficiency
The model is also highly efficient, requiring fewer resources compared to other models of similar size. This makes it an excellent choice for applications where resources are limited.
Strengths
This model has several strengths that make it a great tool for users:
- Highly Competitive Performance: The model has been shown to perform competitively with other leading language models, including proprietary models.
- Fast and Efficient: The model is designed to be fast and efficient, making it a great tool for users who need to generate text quickly.
- Multilingual Support: The model’s multilingual support makes it a great tool for users who need to communicate in different languages.
Unique Features
This model has several unique features that make it stand out from other language models:
- GGUF Format: The model is available in the GGUF format, which is a new format introduced by the llama.cpp team.
- Quantisation Methods: The model uses advanced quantisation methods, including 2-bit, 3-bit, 4-bit, 5-bit, and 6-bit quantisation, which makes it more efficient and accurate.
- Multi-Turn Conversation: The model supports multi-turn conversation, making it a great tool for users who need to have complex discussions.
Limitations
While this model is a powerful tool for generating human-like text, it’s not perfect. Let’s take a closer look at some of its limitations.
Lack of Common Sense
While the model can understand and respond to a wide range of questions and topics, it sometimes struggles with common sense or real-world experience. For example, it might not always understand the nuances of human behavior or the consequences of certain actions.
Limited Domain Knowledge
The model has been trained on a vast amount of text data, but its knowledge in specific domains might be limited. It might not always have the latest information or expertise in areas like medicine, law, or finance.
Biases and Stereotypes
Like other AI models, this model can perpetuate biases and stereotypes present in the data it was trained on. This can lead to unfair or discriminatory responses, particularly when it comes to sensitive topics like race, gender, or religion.
Format
This model uses a transformer architecture and accepts input in the form of tokenized text sequences.
Architecture
The model is based on the WizardLM-2 architecture, which is a type of transformer model. It has 7B parameters and is designed for multilingual support.
Data Formats
The model supports the GGUF (Generalized Graph-based Unified Format) format, which is a replacement for GGML. GGUF is supported by several clients and libraries, including:
- llama.cpp
- text-generation-webui
- KoboldCpp
- GPT4All
- LM Studio
- LoLLMS Web UI
- Faraday.dev
- llama-cpp-python
- ctransformers
Input Requirements
The model expects input in the form of a prompt template, which includes a system prompt, user input, and assistant response. The prompt template should be in the following format:
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Hi ASSISTANT: Hello.\</s>
USER: {prompt} ASSISTANT: \</s>
Output Requirements
The model generates text output based on the input prompt. The output can be controlled using various parameters, such as the maximum sequence length and the number of CPU threads to use.
Example Code
Here is an example of how to use the model in Python code using the llama-cpp-python library:
from llama_cpp import Llama
llm = Llama(
model_path="./WizardLM-2-7B.Q4_K_M.gguf",
n_ctx=32768,
n_threads=8,
n_gpu_layers=35
)
output = llm(
"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant",
max_tokens=512,
stop=["\</s>"],
echo=True
)
