Alpaca-LoRA 65B GGML
| Property | Value |
|---|---|
| Author | TheBloke |
| License | Other |
| Base Size | 65B parameters |
| Quantization Options | 2-bit to 8-bit |
What is alpaca-lora-65B-GGML?
Alpaca-LoRA 65B GGML is a quantized version of Chan Sung's Alpaca LoRA model, specifically optimized for CPU and GPU inference using llama.cpp. It offers multiple quantization levels from 2-bit to 8-bit, allowing users to balance performance and resource requirements based on their needs.
Implementation Details
The model is available in various quantization formats, including traditional methods (q4_0, q4_1, q5_0, q5_1, q8_0) and new k-quant methods (q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K). File sizes range from 27.33GB to 48.97GB depending on the quantization method used.
- Supports multiple inference frameworks including text-generation-webui, KoboldCpp, and GPT4All-UI
- Offers GPU offloading capabilities to optimize RAM usage
- Implements new k-quant methods for improved efficiency
Core Capabilities
- Efficient local deployment on consumer hardware
- Flexible quantization options for different hardware constraints
- Compatible with popular inference frameworks
- Support for context windows up to 2048 tokens
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its variety of quantization options and optimization for local deployment, allowing users to run a 65B parameter model on consumer hardware with different efficiency-quality tradeoffs.
Q: What are the recommended use cases?
The model is ideal for users who need to run large language models locally with limited resources. Different quantization levels allow for deployment on various hardware configurations, from systems with limited RAM to more powerful workstations.
