Vigogne 2 13B Instruct GGUF
The Vigogne 2 13B Instruct GGUF model is a French instruction-following AI that's been fine-tuned from the LLaMA-2-13B model. But what does that mean for you? Essentially, it's designed to understand and respond to instructions in French. This model is part of a new format called GGUF, which offers better tokenization and support for special tokens compared to its predecessor, GGML. The model comes in various sizes, ranging from 2-bit to 8-bit quantization, allowing you to choose the right balance between quality and size for your needs. With its ability to handle instructions and conversations in French, this model is a valuable tool for anyone looking to interact with AI in the French language. But how does it perform? The model's performance is influenced by the chosen quantization method, with higher bits generally offering better quality but also increasing the model's size. The 4-bit and 5-bit models are recommended for a balanced quality and size. Overall, the Vigogne 2 13B Instruct GGUF model is a powerful tool for French language AI interactions, offering a range of options to suit different needs and use cases.
Table of Contents
Model Overview
The Vigogne 2 13B Instruct model is a French instruction-following AI model. It’s based on the LLaMA-2-13B model and has been fine-tuned to understand and respond to instructions in French.
Key Features
- Instruction-following: This model is designed to follow instructions and complete tasks in French.
- Language: The model is trained on French language data and is optimized for French instruction-following tasks.
- Size: The model has 13 billion parameters, making it a large and powerful language model.
- Quantization: The model is available in different quantization formats, including 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit, which can be used to balance quality and size.
Capabilities
The Vigogne 2 13B Instruct model is a powerful tool that can follow instructions in French. It’s designed to understand and respond to tasks and requests, and can generate text based on the input prompt.
What can it do?
- Follow instructions in French
- Understand and respond to tasks and requests
- Generate text based on the input prompt
How does it work?
The model uses a technique called quantization to reduce the size of the model while maintaining its performance. This allows it to be used on devices with limited resources.
What are the benefits?
- Smaller model size makes it easier to download and use
- Maintains high performance and accuracy
- Can be used on devices with limited resources
What are the use cases?
- Chatbots and conversational AI
- Language translation and localization
- Text generation and summarization
How to use it?
You can use the Vigogne 2 13B Instruct model with various libraries and frameworks, such as llama-cpp-python and ctransformers. You can also use it with LangChain, a popular library for building conversational AI applications.
Performance
The Vigogne 2 13B Instruct model offers a range of performance options to suit different needs. The model’s speed is influenced by the quantization method used.
Speed
The model’s speed is influenced by the quantization method used. Here are some examples:
| Quantization Method | Bits | Size | Max RAM Required |
|---|---|---|---|
| Q2_K | 2 | 5.43 GB | 7.93 GB |
| Q3_K_S | 3 | 5.66 GB | 8.16 GB |
| Q4_K_M | 4 | 7.87 GB | 10.37 GB |
| Q5_K_M | 5 | 9.23 GB | 11.73 GB |
| Q6_K | 6 | 10.68 GB | 13.18 GB |
Accuracy
The model’s accuracy is closely tied to the quantization method used. Here’s a rough estimate of the quality loss associated with each method:
| Quantization Method | Quality Loss |
|---|---|
| Q2_K | Significant quality loss |
| Q3_K_S | High quality loss |
| Q4_K_M | Balanced quality |
| Q5_K_M | Low quality loss |
| Q6_K | Extremely low quality loss |
Efficiency
The model’s efficiency is also influenced by the quantization method used. Here are some examples of how the model’s performance changes with different quantization methods:
| Quantization Method | Performance |
|---|---|
| Q2_K | Fast, but with significant quality loss |
| Q3_K_S | Faster than Q4_K_M, but with higher quality loss |
| Q4_K_M | Balanced performance and quality |
| Q5_K_M | Slower than Q4_K_M, but with lower quality loss |
| Q6_K | Slowest, but with extremely low quality loss |
Limitations
The Vigogne 2 13B Instruct model is a powerful tool, but it’s not perfect. Let’s talk about some of its limitations.
Quantization Limitations
The model uses quantization to reduce its size and improve performance. However, this comes at a cost. The quantized models may not be as accurate as the original model. The amount of quality loss depends on the quantization method used.
Compatibility Issues
The model is compatible with certain clients and libraries, but it may not work with others. Make sure to check the compatibility list before using the model.
RAM Requirements
The model requires a significant amount of RAM to run, especially if you’re using a large model file. Make sure your system has enough RAM to handle the model.
GPU Acceleration
The model can be accelerated using a GPU, but it’s not required. If you don’t have a GPU, you can still use the model, but it may be slower.
Training Data
The model was trained on a specific dataset, which may not cover all possible scenarios. This means that the model may not perform well on tasks that are outside its training data.
Fine-Tuning
The model can be fine-tuned for specific tasks, but this requires a significant amount of data and computational resources.
Support
If you encounter any issues with the model, you can join the Discord server or Patreon page for support.
Format
The Vigogne 2 13B Instruct model uses the GGUF (Generalized General-purpose Unified Format) format, a new format introduced by the llama.cpp team. This format offers several advantages over the older GGML format, including better tokenization, support for special tokens, and extensibility.
Supported Data Formats
The GGUF format supports various quantization methods, including:
- 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit quantization
- Different quantization methods, such as Q2_K, Q3_K_S, Q3_K_M, Q3_K_L, Q4_0, Q4_K_S, Q4_K_M, Q5_0, Q5_K_S, Q5_K_M, and Q6_K
Input and Output Requirements
- Input: Tokenized text sequences
- Output: Response to the input prompt
Special Requirements
- Requires a specific pre-processing step for input prompts
- Supports GPU acceleration for faster processing
Example Code
To use the Vigogne 2 13B Instruct model, you can use the following example code:
import ctransformers
# Load the model
llm = ctransformers.AutoModelForCausalLM.from_pretrained(
"TheBloke/Vigogne-2-13B-Instruct-GGUF",
model_file="vigogne-2-13b-instruct.q4_K_M.gguf",
model_type="llama",
gpu_layers=50
)
# Generate a response to a prompt
prompt = "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"
response = llm(prompt)
print(response)
Note: This code example assumes you have the ctransformers library installed and have downloaded the Vigogne 2 13B Instruct model file.
