DeepCoder-14B-Preview-GGUF
| Property | Value |
|---|---|
| Base Model Size | 14B parameters |
| Author | bartowski |
| Original Source | agentica-org/DeepCoder-14B-Preview |
| Format | GGUF with imatrix quantization |
What is DeepCoder-14B-Preview-GGUF?
DeepCoder-14B-Preview-GGUF is a comprehensive collection of quantized versions of the original DeepCoder model, specifically optimized for efficient deployment across various hardware configurations. The model utilizes advanced imatrix quantization techniques to provide multiple compression levels while maintaining performance.
Implementation Details
The model is available in various quantization formats ranging from bf16 (29.55GB) to IQ2_XS (4.70GB), each optimized for different use cases and hardware constraints. It implements a specific prompt format using special tokens and includes optimizations for both CPU and GPU inference.
- Utilizes llama.cpp release b5074 for quantization
- Supports various quantization levels (Q2 to Q8)
- Features special embed/output weight handling in certain variants
- Includes online repacking capability for ARM CPU inference
Core Capabilities
- Multiple quantization options for different performance/size trade-offs
- Optimized versions for both high-end and resource-constrained environments
- Special handling for embedding and output weights in specific variants
- Support for various inference backends including CPU and GPU
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its comprehensive range of quantization options using imatrix technology, allowing users to choose the perfect balance between model size and performance for their specific use case. It includes specialized variants with Q8_0 quantization for embedding and output weights, improving quality in critical areas.
Q: What are the recommended use cases?
For most users, the Q4_K_M variant (8.99GB) offers a good balance of quality and size. Users with limited RAM should consider the IQ3/IQ2 variants, while those requiring maximum quality should opt for Q6_K_L or higher quantizations. The choice depends on available hardware resources and quality requirements.
