TheDrummer Fallen-Llama 3.3 70B GGUF
| Property | Value |
|---|---|
| Base Model | Fallen-Llama 3.3 70B |
| Quantization Types | Multiple (Q8_0 to IQ1_M) |
| Size Range | 16.75GB - 74.98GB |
| Original Model Link | huggingface.co/TheDrummer/Fallen-Llama-3.3-R1-70B-v1 |
What is TheDrummer_Fallen-Llama-3.3-R1-70B-v1-GGUF?
This is a comprehensive collection of quantized versions of the Fallen-Llama 3.3 70B model, created using llama.cpp's latest quantization techniques. The collection offers various compression levels to accommodate different hardware configurations and use cases, from extremely high-quality 75GB versions to compact 16GB implementations.
Implementation Details
The model uses advanced imatrix quantization techniques and offers multiple quantization formats including standard K-quants and newer I-quants. Each version is optimized for specific use cases, with some variants featuring special Q8_0 quantization for embedding and output weights.
- Utilizes llama.cpp release b4792 for quantization
- Implements online repacking for ARM and AVX CPU inference
- Supports various backends including cuBLAS, rocBLAS, and Metal
- Features special prompt format with system prompts and user/assistant markers
Core Capabilities
- Multiple quality tiers from extremely high (Q8_0) to basic (IQ1_M)
- Optimized performance for different hardware configurations
- Support for split files in larger quantizations
- Compatibility with LM Studio and other llama.cpp-based projects
Frequently Asked Questions
Q: What makes this model unique?
This model offers an unprecedented range of quantization options for the Fallen-Llama 70B model, allowing users to choose the perfect balance between quality and resource usage for their specific needs. The implementation of both K-quants and I-quants provides flexibility for different hardware configurations.
Q: What are the recommended use cases?
For maximum quality, use Q8_0 or Q6_K versions if you have sufficient RAM. For balanced performance, Q4_K_M is recommended as the default choice. For systems with limited resources, the IQ3 and IQ2 variants offer surprisingly usable performance while requiring significantly less memory.
