Granite 20b Code Instruct GGUF
Granite 20b Code Instruct GGUF is a versatile AI model designed to handle various tasks efficiently. What sets it apart is its range of quantization options, offering trade-offs between model size, quality, and speed. With multiple file sizes to choose from, you can select the one that best fits your system's RAM and VRAM, ensuring optimal performance. The model's unique feature is its ability to be used on different hardware, including CPU, GPU, and Apple Metal, although some quantization options may have better performance on specific hardware. Whether you prioritize speed, quality, or a balance between the two, Granite 20b Code Instruct GGUF provides a flexible solution for various use cases.
Table of Contents
Model Overview
The Granite-20b-Code-Instruct model is a powerful tool for natural language processing tasks. But what makes it so special?
Key Attributes
- Large Model: With
20Bparameters, this model is capable of handling complex tasks with ease. - Quantization Options: The model comes in various quantization formats, including
f16,Q8_0,Q6_K_L, and more. These formats allow you to balance quality and file size to suit your needs. - File Sizes: The model files range from
8.00GBto40.24GB, making it accessible to users with varying amounts of storage space.
Functionalities
- Code Instruct: The model is designed to understand and generate code, making it a valuable tool for developers and programmers.
- Natural Language Processing: The model can handle a wide range of natural language processing tasks, from text classification to language translation.
Capabilities
The Granite-20b-Code-Instruct model is a powerful tool for generating text and code. But what makes it special?
Primary Tasks
This model is designed to perform a variety of tasks, including:
- Generating code in multiple programming languages
- Answering questions on a wide range of topics
- Completing tasks that require a deep understanding of language and context
Strengths
So, what sets the Granite-20b-Code-Instruct model apart from others like ==Other Models==? Here are a few key strengths:
- High-quality text generation: This model is capable of generating text that is coherent, engaging, and often indistinguishable from text written by a human.
- Code generation: The model can generate code in multiple programming languages, making it a valuable tool for developers and programmers.
- Contextual understanding: The Granite-20b-Code-Instruct model has a deep understanding of language and context, allowing it to complete tasks that require a high degree of nuance and complexity.
Performance
Granite-20b-Code-Instruct is a powerful AI model that showcases remarkable performance in various tasks. Let’s dive into its speed, accuracy, and efficiency.
Speed
The model’s speed is influenced by the type of quantization used. For instance, the Q4_0_X_X quants offer a substantial speedup on ARM chips. If you’re using an ARM chip, you can expect a significant boost in performance.
| Quant Type | File Size | Speed |
|---|---|---|
| Q4_0_4_4 | 11.55GB | Fast |
| Q4_0_4_8 | 11.55GB | Fast |
| Q4_0_8_8 | 11.55GB | Fast |
Accuracy
The model’s accuracy is also dependent on the quantization type. Some quants, like Q8_0, offer extremely high quality and are generally unneeded but provide the maximum available quantization. Others, like Q3_K_S, have lower quality but are still surprisingly usable.
| Quant Type | File Size | Accuracy |
|---|---|---|
| Q8_0 | 21.48GB | Extremely High |
| Q6_K_L | 16.71GB | Very High |
| Q3_K_S | 8.93GB | Low |
Limitations
Granite-20b-Code-Instruct is a powerful tool, but it’s not perfect. Let’s talk about some of its limitations.
Quantization Limitations
The model’s performance can vary greatly depending on the quantization method used. Some quants, like Q8_0, are extremely high quality but may not be suitable for all devices due to their large file size. On the other hand, smaller quants like Q2_K may be more usable but sacrifice quality.
Device Compatibility
Not all devices are created equal. Granite-20b-Code-Instruct may not run smoothly on devices with limited RAM or VRAM. You’ll need to choose a quant that fits your device’s capabilities.
Quality Trade-Offs
There’s a trade-off between quality and speed. If you want the absolute maximum quality, you may need to sacrifice some speed. Conversely, if you want the model to run as fast as possible, you may need to settle for lower quality.
Format
Granite-20b-Code-Instruct uses a transformer architecture and accepts input in the form of tokenized text sequences. The model supports various quantization formats, including:
| Quant Type | File Size | Description |
|---|---|---|
| f16 | 40.24GB | Full F16 weights |
| Q8_0 | 21.48GB | Extremely high quality, generally unneeded but max available quant |
| Q6_K_L | 16.71GB | Uses Q8_0 for embed and output weights. Very high quality, near perfect, recommended |
| … | … | … |
Input Format
The model expects input in the following format:
{
"system_prompt": "System prompt",
"prompt": "Question or prompt",
"answer": "Answer"
}
For example:
{
"system_prompt": "Tell me a joke",
"prompt": "Why was the math book sad?",
"answer": "Because it had too many problems"
}
Output Format
The model produces output in the following format:
{
"answer": "Answer"
}
For example:
{
"answer": "Because it had too many problems"
}
Special Requirements
- The model requires a significant amount of memory (RAM and/or VRAM) to run efficiently. It’s recommended to choose a quantization format that fits within 1-2GB of your available memory.
- The model can be run on CPU, GPU, or Apple Metal, but performance may vary depending on the hardware and quantization format chosen.
- Some quantization formats (e.g. I-quants) may not be compatible with certain hardware or software configurations.
