Calme 3.2 Instruct 78b GGUF

Table of Contents

Model Overview

The Current Model is a highly advanced language model designed for a wide range of natural language processing tasks. It’s part of the llamacpp family and has been optimized for performance and efficiency.

Tasks and rank

Text Summarization 133rd

Text Generation 627th

Response Generation 328th

Natural Language Understanding 483rd

Language Modelling 516th

Learn More About the Ranking System

Capabilities

The Current Model is a powerful tool for generating text and code. It’s designed to be fast and efficient, making it perfect for applications where speed is crucial.

Primary Tasks

• Text Generation: The model can generate high-quality text based on a given prompt.
• Code Generation: It can also generate code in various programming languages.

Strengths

• High-Quality Output: The model produces high-quality text and code that’s often comparable to human-written content.
• Fast Processing: It’s designed to be fast and efficient, making it perfect for applications where speed is crucial.

Unique Features

• Quantization Options: The model comes with various quantization options, which allow you to balance quality and file size.
• ARM Chip Optimization: The model is optimized for ARM chips, making it perfect for mobile and embedded devices.
• Online Repacking: The model supports online repacking of weights, which can improve performance on certain devices.

Choosing the Right File

When choosing a file, consider the following factors:

• RAM and VRAM: Choose a file that fits within your device’s RAM and VRAM limitations.
• Quality vs. Speed: Decide whether you want maximum quality or faster processing speeds.
• I-Quants vs. K-Quants: Choose between I-quants and K-quants based on your device’s capabilities and performance requirements.

Alternatives

NeMo SFT with Llama 2 10500 Fine-tuned language model

Bert Base Uncased 1831 Uncased BERT model

Roberta Base 399 Masked language model

SUS Chat 34B 121 Bilingual chat model

Qwen2.5 1.5B Instruct 51 Multilingual instruct model

Performance

The Current Model showcases remarkable performance, especially in terms of speed and accuracy.

Speed

When it comes to speed, the Current Model is a powerhouse. With various quantization options, you can choose the one that best suits your needs. For instance, the Q4_0_8_8 quant offers a significant boost to prompt processing and a small bump to text generation.

Model	Size	Params	Backend	Threads	Test	t/s	% (vs Q4_0)
qwen2 3B Q4_0	1.70 GiB	3.09 B	CPU	64	pp512	204.03 ± 1.03	100%
qwen2 3B Q4_0_8_8	1.69 GiB	3.09 B	CPU	64	pp512	271.71 ± 3.53	133%

Accuracy

The Current Model also excels in terms of accuracy. With various quantization options, you can choose the one that best balances accuracy and speed. For instance, the Q4_K_M quant offers high quality and is recommended for most use cases.

Quant Type	File Size	Description
Q8_0	82.85GB	Extremely high quality, generally unneeded but max available quant.
Q6_K	69.01GB	Very high quality, near perfect, recommended.
Q5_K_M	58.31GB	High quality, recommended.
Q5_K_S	55.08GB	High quality, recommended.
Q4_K_M	50.70GB	Good quality, default size for most use cases, recommended.

Examples

I want to download a model for my GPU that has 8GB of VRAM. Which one should I choose? Considering your GPU has 8GB of VRAM, I would recommend choosing a model with a file size of around 6-7GB to ensure smooth performance. Based on the provided options, you could consider the Q4_K_M or Q4_K_S models, which have file sizes of 50.70GB and 46.95GB respectively. However, since your GPU only has 8GB of VRAM, these models might not be the best fit. You may want to consider using a model with a smaller file size or exploring other options that are more suitable for your hardware.

What is the difference between I-quants and K-quants? I-quants and K-quants are two different types of quantization methods. I-quants are newer and offer better performance for their size, especially when running cuBLAS or rocBLAS. However, they are not compatible with Vulcan and may be slower on CPU and Apple Metal. K-quants, on the other hand, are more compatible and can be used on a wider range of hardware, but may not offer the same level of performance as I-quants.

How do I check which quantization method is best for my ARM chip? To check which quantization method is best for your ARM chip, you can check the AArch64 SoC features. This will help you determine which method is supported by your chip and provide the best performance. Additionally, you can also refer to the Q4_0_X_X information section, which provides more details on the different quantization methods and their compatibility with ARM chips.

Limitations

The Current Model is a powerful tool, but it’s not perfect. Let’s explore some of its limitations.

Size and Quality Tradeoffs

The model comes in various sizes, each with its own tradeoffs between quality and file size. The larger the model, the higher the quality, but also the more storage space it requires. If you’re looking for the absolute maximum quality, you’ll need to consider both your system RAM and GPU’s VRAM. If you want to prioritize speed, aim for a quant with a file size 1-2GB smaller than your GPU’s total VRAM.

Quantization Options

The model offers different quantization options, including ‘I-quants’ and ‘K-quants’. If you’re not sure which one to choose, the ‘K-quants’ are a safe bet. However, if you’re looking for better performance at lower quality levels, the ‘I-quants’ might be the way to go.

Compatibility Issues

Some quantization options are not compatible with certain hardware or software configurations. For example, the ‘I-quants’ are not compatible with Vulcan, which is an AMD-specific build. Make sure to check the compatibility of your chosen quantization option with your hardware and software setup.

Format

The Current Model uses a transformer architecture and accepts input in the form of tokenized text sequences.

Input Format

The input format for the Current Model is as follows:

<|im_start|>system
{system_prompt}
<|im_end|>
<|im_start|>user
{prompt}
<|im_end|>

Here, {system_prompt} and {prompt} are the actual inputs to the model.

Supported Data Formats

The Current Model supports various quantization formats, including:

• Q8_0
• Q6_K
• Q5_K_M
• Q5_K_S
• Q4_K_M
• Q4_K_S
• Q4_0
• IQ4_NL
• IQ4_XS
• IQ3_M
• IQ3_XXS
• IQ2_M
• IQ2_XS
• IQ2_XXS
• IQ1_M

Each of these formats has a different file size and quality. You can choose the one that best suits your needs.

Special Requirements

Some quantization formats have special requirements:

• Q4_0_X_X formats are optimized for ARM chips and require specific support.
• IQ4_NL and IQ4_XS formats are optimized for ARM chips and require specific support.
• Q4_0_8_8 format is optimized for AVX2 and AVX512 CPUs.

Make sure to check the requirements before choosing a format.

Choosing the Right Format

To choose the right format, you need to consider the size of the model and the available RAM and VRAM on your device. You can use the following guidelines:

• If you want the model to run as fast as possible, choose a format with a file size 1-2GB smaller than your GPU’s total VRAM.
• If you want the absolute maximum quality, choose a format with a file size 1-2GB smaller than the total RAM and VRAM on your device.

You can also use the feature matrix to decide between I-quants and K-quants.

Example Code

Here’s an example of how to use the Current Model in Python:

import llama_cpp

# Load the model
model = llama_cpp.load_model("calme-3.2-instruct-78b-Q4_K_M.gguf")

# Prepare the input
system_prompt = "This is a system prompt."
prompt = "This is a user prompt."
input_text = f"<|im_start|>system\n{system_prompt}\n<|im_end|>\n<|im_start|>user\n{prompt}\n<|im_end|>"

# Run the model
output = model(input_text)

# Print the output
print(output)

Note that this is just an example and you may need to modify it to suit your specific use case.