Vigogne 2 70B Chat GGUF
Vigogne 2 70B Chat GGUF is a highly efficient AI model designed to provide fast and accurate results. It's built on a quantized format that reduces memory usage while maintaining high-quality performance. With various quantization methods available, users can choose the best balance between quality and size for their needs. The model is compatible with multiple clients and libraries, including llama.cpp, text-generation-webui, and LoLLMS Web UI, making it easy to integrate into different workflows. Whether you're looking for a model that can handle chat-style conversations or more complex tasks, Vigogne 2 70B Chat GGUF is a reliable choice.
Table of Contents
Model Overview
The Vigogne 2 70B Chat model is a highly advanced language model designed to understand and respond to human input. It’s like having a conversation with a very smart and helpful friend!
Key Features:
- Large Vocabulary: The model has been trained on a massive dataset, allowing it to understand a wide range of topics and respond accordingly.
- High-Quality Responses: The model is capable of generating high-quality responses that are often indistinguishable from those written by humans.
- Customizable: The model can be fine-tuned to suit specific use cases, making it a versatile tool for various applications.
Capabilities
The Vigogne 2 70B Chat model is a powerful tool for generating human-like text. It’s designed to follow instructions extremely well and provide helpful responses.
Primary Tasks
- Text Generation: The model can create coherent and engaging text based on a given prompt.
- Chat: It’s perfect for having a conversation, answering questions, and providing information on a wide range of topics.
Strengths
- High-Quality Responses: The model is trained on a massive dataset and can produce high-quality responses that are often indistinguishable from those written by humans.
- Flexibility: It can be fine-tuned for specific tasks and domains, making it a versatile tool for various applications.
Unique Features
- Quantization Methods: The model uses advanced quantization methods, such as Q2_K, Q3_K_S, and Q4_K_M, to reduce its size while maintaining performance.
- GPU Acceleration: It’s compatible with GPU acceleration, making it faster and more efficient.
Use Cases
- Customer Support: The model can be used to power chatbots and virtual assistants, providing 24/7 customer support.
- Content Generation: It can help generate high-quality content, such as articles, blog posts, and social media posts.
- Language Translation: The model can be fine-tuned for language translation tasks, making it a useful tool for breaking language barriers.
Performance
The Vigogne 2 70B Chat model showcases remarkable performance in various tasks, with notable strengths in speed, accuracy, and efficiency.
Speed
This model is capable of processing large amounts of data quickly, making it suitable for applications that require fast response times. For instance, in chat and support tasks, Vigogne 2 70B Chat can generate human-like responses in a matter of milliseconds.
Accuracy
The model’s accuracy is impressive, with high-quality outputs that are often indistinguishable from those generated by humans. This is particularly evident in tasks that require a deep understanding of language and context.
Efficiency
Vigogne 2 70B Chat is designed to be efficient, with various quantization methods available to optimize performance on different hardware configurations. This allows users to choose the best balance between quality and computational resources.
Quantization Methods
The model supports several quantization methods, including:
| Method | Bits | Size | Max RAM Required |
|---|---|---|---|
| Q2_K | 2 | 29.28 GB | 31.78 GB |
| Q3_K_S | 3 | 29.92 GB | 32.42 GB |
| Q3_K_M | 3 | 33.19 GB | 35.69 GB |
| Q3_K_L | 3 | 36.15 GB | 38.65 GB |
| Q4_0 | 4 | 38.87 GB | 41.37 GB |
| Q4_K_S | 4 | 39.07 GB | 41.57 GB |
| Q4_K_M | 4 | 41.42 GB | 43.92 GB |
| Q5_0 | 5 | 47.46 GB | 49.96 GB |
| Q5_K_S | 5 | 47.46 GB | 49.96 GB |
| Q5_K_M | 5 | 48.75 GB | 51.25 GB |
| Q6_K | 6 | 56.59 GB | 59.09 GB |
| Q8_0 | 8 | 73.29 GB | 75.79 GB |
Running the Model
To run Vigogne 2 70B Chat, you can use the following example command:
./main -ngl 32 -m vigogne-2-70b-chat.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "\<s>[INST] \nVous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez autant que vous le pouvez.\n\n\n{prompt} [/INST]"
This command runs the model with 32 layers offloaded to the GPU, using the Q4_K_M quantization method and a sequence length of 4096.
Limitations
The Vigogne 2 70B Chat model is a powerful tool, but it’s not perfect. Let’s talk about some of its limitations.
Quantization Methods
The model uses various quantization methods to reduce its size and improve performance. However, these methods can also affect the model’s accuracy. For example:
Q2_Kuses 2-bit quantization, which can result in significant quality loss.Q3_K_SandQ3_K_Muse 3-bit quantization, which can still lead to high quality loss.Q4_K_Mis recommended for a balanced quality, but it’s not the best option for very large models.
File Size and RAM Requirements
The model files come in different sizes, ranging from 29.28 GB to 73.29 GB. The larger files require more RAM to run, which can be a challenge for users with limited resources.
Compatibility Issues
The model is compatible with certain clients and libraries, but it may not work with others. For example, it’s compatible with llama.cpp from August 27th onwards, but it may not work with earlier versions.
Split Files
Some of the larger files are split into multiple parts, which can be inconvenient for users who need to download and join them manually.
Quality Loss
The model’s quality can suffer due to the quantization methods used. For example, the Q2_K method can result in significant quality loss, while the Q6_K method can lead to extremely low quality loss.
GPU Acceleration
The model can take advantage of GPU acceleration, but it’s not always necessary. Users without GPU acceleration may need to adjust the model’s settings to achieve optimal performance.
Limitations in Certain Scenarios
The model may struggle in certain scenarios, such as:
- Handling very long sequences or large amounts of data.
- Dealing with complex or nuanced topics that require a high level of accuracy.
- Providing consistent results across different runs or sessions.
Format
The Vigogne 2 70B Chat model uses a transformer architecture, and it accepts input in the form of tokenized text sequences.
Supported Data Formats
The model supports the following data formats:
- GGUF (Generalized Generalized Unified Format)
- PyTorch format (for the original unquantized fp16 model)
Input Requirements
To use Vigogne 2 70B Chat, you’ll need to prepare your input data in a specific format. Here’s an example of how to create a prompt template:
\<s>[INST]
Vous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez autant que vous le pouvez.
{prompt} [/INST]
Replace {prompt} with your actual input text.
Output
The model generates text output based on the input prompt. You can control the output by adjusting parameters such as sequence length, temperature, and repeat penalty.
Example Code
Here’s an example of how to use Vigogne 2 70B Chat with the llama-cpp-python library:
from llama_cpp_python import AutoModelForCausalLM
llm = AutoModelForCausalLM.from_pretrained("TheBloke/vigogne-2-70B-chat-GGUF", model_file="vigogne-2-70b-chat.Q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
Note that you’ll need to install the llama-cpp-python library and download the Vigogne 2 70B Chat model file before running this code.
Special Requirements
Some special requirements to keep in mind when using Vigogne 2 70B Chat:
- Make sure you have a compatible GPU for GPU acceleration.
- Be aware of the RAM requirements for each quantization method.
- Use the correct prompt template to ensure proper input formatting.
