DeepHermes-3-Mistral-24B GGUF Quantized Models
| Property | Value |
|---|---|
| Original Model | NousResearch/DeepHermes-3-Mistral-24B-Preview |
| Quantization Framework | llama.cpp (b4877) |
| Size Range | 7.21GB - 47.15GB |
| Author | bartowski |
What is NousResearch_DeepHermes-3-Mistral-24B-Preview-GGUF?
This is a comprehensive collection of quantized versions of the DeepHermes-3-Mistral-24B model, optimized for different hardware configurations and use cases. The quantizations range from full BF16 precision to highly compressed IQ2 variants, offering various tradeoffs between model size, inference speed, and output quality.
Implementation Details
The model uses imatrix quantization techniques and comes in multiple variants optimized for different scenarios. The quantization process preserves model quality while reducing size, with special attention paid to embedding and output weights in certain variants.
- Multiple quantization levels from BF16 to IQ2
- Specialized variants with Q8_0 embedding weights for improved quality
- Support for online repacking for ARM and AVX CPU inference
- Compatible with LM Studio and llama.cpp-based projects
Core Capabilities
- High-quality inference with Q6_K_L and Q5_K variants
- RAM-efficient options with Q3 and Q2 quantizations
- Optimized performance on various hardware architectures
- Flexible deployment options from 47GB to 7GB models
Frequently Asked Questions
Q: What makes this model unique?
This model collection offers unprecedented flexibility in deployment, with carefully calibrated quantizations that maintain quality while providing options for nearly any hardware configuration. The use of imatrix quantization and specialized embedding weight handling sets it apart from standard quantized models.
Q: What are the recommended use cases?
For optimal quality, use Q6_K_L or Q5_K variants with sufficient RAM. For balanced performance, Q4_K_M is recommended as the default choice. For limited hardware, IQ3/IQ2 variants offer surprisingly usable performance with minimal resource requirements. ROCm users should prefer K-quants over I-quants.
