open-thoughts_OpenThinker2-7B-GGUF
| Property | Value |
|---|---|
| Original Model | OpenThinker2-7B |
| Quantization Types | Multiple (BF16 to IQ2) |
| Size Range | 2.78GB - 15.24GB |
| Author | bartowski |
| Original Source | Hugging Face |
What is open-thoughts_OpenThinker2-7B-GGUF?
This is a comprehensive collection of GGUF quantized versions of the OpenThinker2-7B model, optimized using llama.cpp's imatrix quantization technique. The collection provides various compression levels to accommodate different hardware capabilities and use-case requirements, ranging from full BF16 precision to highly compressed IQ2 formats.
Implementation Details
The quantization process utilizes llama.cpp release b5035 with imatrix optimization. The model supports a specific prompt format using im_start/im_end tokens for system and user interactions. Notable implementations include special variants with Q8_0 quantization for embedding and output weights in certain versions (Q3_K_XL, Q4_K_L, etc.) to maintain quality in critical model components.
- Multiple quantization options from BF16 (15.24GB) to IQ2_M (2.78GB)
- Advanced online repacking support for ARM and AVX CPU inference
- Specialized embedding/output weight handling in XL/L variants
- Optimized performance through imatrix quantization
Core Capabilities
- Flexible deployment options for various hardware configurations
- High-quality compression with Q6_K_L recommended for optimal performance
- ARM and AVX optimization through online repacking
- Balanced quality-size tradeoffs across different quantization levels
Frequently Asked Questions
Q: What makes this model unique?
This model offers an extensive range of quantization options with carefully optimized compression levels, making it highly versatile for different hardware configurations while maintaining quality through strategic weight handling and modern quantization techniques.
Q: What are the recommended use cases?
For most users, the Q4_K_M variant (4.68GB) is recommended as the default choice. Users with limited RAM should consider Q3_K variants, while those requiring maximum quality should opt for Q6_K_L or Q5_K_L versions. The IQ variants are particularly suitable for newer hardware with specific optimization requirements.
