Calme 2.3 Rys 78b GGUF

Table of Contents

Model Overview

The Current Model is a powerful tool for natural language processing tasks. But what makes it so special?

Tasks and rank

Text Summarization 123rd

Text Generation 527th

Language Modelling 436th

Dialogue Systems 212th

Chatbot 325th

Learn More About the Ranking System

Capabilities

Capable of generating both text and code, this model outperforms many open-source chat models across common industry benchmarks. Let’s dive into its capabilities.

Primary Tasks

The Current Model is designed to excel at:

• Generating human-like text
• Creating code in various programming languages
• Answering questions and providing information on a wide range of topics

Strengths

So, what makes the Current Model so good at these tasks? Here are a few of its key strengths:

• High-quality output: The Current Model is capable of producing text and code that is often indistinguishable from that written by humans.
• Speed: The Current Model can process and respond to input quickly, making it ideal for applications where speed is important.
• Flexibility: The Current Model can be fine-tuned for specific tasks and domains, allowing it to adapt to a wide range of use cases.

Unique Features

But that’s not all - the Current Model also has some unique features that set it apart from other AI models. For example:

• Quantization options: The Current Model offers a range of quantization options, allowing users to choose the perfect balance between quality and file size.
• Support for multiple hardware platforms: The Current Model can run on a variety of hardware platforms, including GPUs and CPUs.

Alternatives

NeMo SFT with Llama 2 10500 Fine-tuned language model

Bert Base Uncased 1831 Uncased BERT model

BLOOMChat 176B V1 365 Multilingual chat model

SUS Chat 34B 121 Bilingual chat model

Qwen2.5 1.5B Instruct 51 Multilingual instruct model

Choosing the Right Quantization Option

So, how do you choose the right quantization option for your needs? Here are a few things to consider:

• File size: How much space do you have available for the model?
• Quality: How important is high-quality output to your application?
• Hardware: What type of hardware will you be running the model on?

Quantization Options

Here’s a summary of the available quants, their file sizes, and descriptions:

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.
Q4_K_L	51.62GB	Good quality, recommended.
IQ4_XS	42.56GB	Decent quality, smaller than Q4_K_S with similar performance, recommended.

Performance

The Current Model shows remarkable performance in various tasks, offering a balance between speed, accuracy, and efficiency. Let’s dive into the details.

Speed

The model’s speed is influenced by the quantization type and file size. For the fastest performance, it’s recommended to choose a quant with a file size 1-2GB smaller than your GPU’s total VRAM.

Accuracy

The model’s accuracy varies depending on the quantization type. The higher-quality quants, such as Q8_0 and Q6_K, offer extremely high accuracy, while the lower-quality quants, like Q2_K and IQ1_M, have relatively lower accuracy.

Efficiency

The model’s efficiency is also affected by the quantization type. The I-quants (IQX_X) offer better performance for their size, especially when running on cuBLAS (Nvidia) or rocBLAS (AMD). However, they may be slower on CPU and Apple Metal.

Examples

What is the recommended file size to choose for a model to run as fast as possible on a GPU with 8GB VRAM? Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM, so 6-7GB.

What is the difference between I-quants and K-quants? K-quants are in format 'QX_K_X', like Q5_K_M, and are recommended if you don't want to think too much. I-quants are in format IQX_X, like IQ3_M, and offer better performance for their size, but are not compatible with Vulcan.

How do I download a specific file from the repository using huggingface-cli? Use the command 'huggingface-cli download bartowski/calme-2.3-rys-78b-GGUF --include "calme-2.3-rys-78b-Q4_K_M.gguf" --local-dir./'. Replace 'calme-2.3-rys-78b-Q4_K_M.gguf' with the desired file name.

Limitations

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

Quality Trade-Offs

When using the Current Model, you’ll need to balance quality and file size. The larger the file size, the higher the quality, but also the more RAM and VRAM required.

Quantization Options

There are two main types of quants: K-quants and I-quants. K-quants are more established, but I-quants offer better performance for their size, especially below Q4. However, I-quants are not compatible with Vulcan (AMD) and may be slower on CPU and Apple Metal.

Performance Variations

The performance of the Current Model can vary greatly depending on the hardware and quantization method used. For instance, using cuBLAS (Nvidia) or rocBLAS (AMD) can improve performance, but using Vulcan (AMD) may lead to compatibility issues.

Format

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

Input Format

To interact with the Current Model, you’ll need to format your input in a specific way. Here’s an example:

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

Replace {system_prompt} with your system prompt and {prompt} with your user prompt.

Supported Data Formats

The Current Model supports various data formats, including:

Format	Description
Q8_0	Extremely high quality, generally unneeded but max available quant.
Q6_K	Very high quality, near perfect, recommended.
Q5_K_M	High quality, recommended.
Q4_K_L	Good quality, recommended.
Q4_K_M	Good quality, default size for most use cases, recommended.

Special Requirements

When choosing a format, consider the following:

• If you want the absolute maximum quality, add both your system RAM and your GPU’s VRAM together, then grab a quant with a file size 1-2GB smaller than that total.
• If you want your model running as fast as possible, you’ll want to fit the whole thing on your GPU’s VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU’s total VRAM.
• Decide whether to use an ‘I-quant’ or a ‘K-quant’ based on your specific needs. I-quants offer better performance for their size but may be slower on CPU and Apple Metal.

Output Format

The output format for the Current Model is a text sequence.