Llama-3-8B-Instruct-Coder-v2-GGUF
| Property | Value |
|---|---|
| Author | bartowski |
| Base Model | Llama-3-8B-Instruct-Coder-v2 |
| Model Type | Instruction-tuned Code Generation |
| Quantization Options | 23 variants (Q8_0 to IQ1_S) |
| Original Source | huggingface.co/rombodawg/Llama-3-8B-Instruct-Coder-v2 |
What is Llama-3-8B-Instruct-Coder-v2-GGUF?
This is a specialized collection of quantized versions of the Llama-3-8B-Instruct-Coder-v2 model, optimized for code generation tasks. The repository offers 23 different quantization variants, ranging from the highest quality 8.54GB Q8_0 version to the smallest 2.01GB IQ1_S version, allowing users to balance between performance and resource requirements.
Implementation Details
The model uses llama.cpp's imatrix quantization technology, with various quantization methods including K-quants and I-quants. Each variant is optimized using specific techniques to maintain performance while reducing model size, with special attention to compatibility with different hardware acceleration options like cuBLAS, rocBLAS, and Vulkan.
- Supports multiple quantization levels (Q8_0 to IQ1_S)
- Uses advanced imatrix quantization techniques
- Compatible with various hardware acceleration options
- Includes specialized prompt format for optimal interaction
Core Capabilities
- Code generation and completion
- Flexible deployment options across different hardware configurations
- Memory-efficient operation through various quantization options
- Instruction-following capabilities for coding tasks
Frequently Asked Questions
Q: What makes this model unique?
This model offers an exceptional range of quantization options, allowing users to find the perfect balance between model quality and resource usage. It's specifically optimized for code-related tasks and uses state-of-the-art quantization techniques.
Q: What are the recommended use cases?
The model is ideal for code generation, completion, and instruction-following tasks. For optimal performance, users with sufficient VRAM should choose Q6_K or Q5_K_M variants, while those with limited resources can opt for the smaller I-quant versions which offer good performance-to-size ratio.
