Llama-3-8B-Instruct-32k-v0.1-GGUF
| Property | Value |
|---|---|
| Parameter Count | 8.03B |
| Model Type | Instruction-tuned Language Model |
| Format | GGUF (Multiple Quantization Options) |
| Context Length | 32,000 tokens |
| Author | MaziyarPanahi |
What is Llama-3-8B-Instruct-32k-v0.1-GGUF?
This is a quantized version of the LLaMA-3 8B parameter model, specifically optimized for instruction-following tasks with an extended context window of 32,000 tokens. The model is available in various GGUF (GPT-Generated Unified Format) quantization levels, ranging from 2-bit to 8-bit precision, offering different tradeoffs between model size and performance.
Implementation Details
The model represents a significant advancement in accessible AI deployment, utilizing the GGUF format which replaced the older GGML standard. It's designed for efficient local deployment and is compatible with numerous popular inference frameworks and UIs.
- Multiple quantization options (2-bit to 8-bit) for different deployment scenarios
- Optimized for instruction-following tasks
- Extended 32k token context window
- GGUF format for improved compatibility and performance
Core Capabilities
- Text generation and completion
- Instruction following and task completion
- Conversational AI applications
- Long-context processing (up to 32k tokens)
- Efficient local deployment across various platforms
Frequently Asked Questions
Q: What makes this model unique?
This model combines the capabilities of LLaMA-3 architecture with an extended context window and multiple quantization options, making it highly versatile for different deployment scenarios. The GGUF format ensures broad compatibility with popular frameworks like llama.cpp, text-generation-webui, and others.
Q: What are the recommended use cases?
The model is particularly well-suited for applications requiring instruction following, long-context understanding, and efficient local deployment. It's ideal for chatbots, text completion, and other generative AI tasks where a balance between performance and resource usage is crucial.
