OrcaAgent-llama3.2-1b-GGUF
| Property | Value |
|---|---|
| Parameter Count | 1.24B |
| License | Apache 2.0 |
| Base Model | Isotonic/OrcaAgent-llama3.2-1b |
| Quantized By | mradermacher |
What is OrcaAgent-llama3.2-1b-GGUF?
OrcaAgent-llama3.2-1b-GGUF is a quantized version of the Orca model specifically designed for agent-based instruction following. Built on the Llama 3.2 architecture, this model has been trained on the microsoft/orca-agentinstruct-1M-v1 and Isotonic/agentinstruct-1Mv1-combined datasets to enhance its instruction-following capabilities.
Implementation Details
The model offers multiple quantization options optimized for different use cases, ranging from lightweight 0.7GB implementations to full 2.6GB versions. Notable quantization variants include Q4_K_S and Q4_K_M which are recommended for their optimal balance of speed and quality, while Q8_0 provides the highest quality at 1.4GB.
- Multiple quantization options from Q2_K to f16
- File sizes ranging from 0.7GB to 2.6GB
- Optimized for text generation inference
- Compatible with transformers library
Core Capabilities
- Agent-based instruction following
- Efficient text generation
- Optimized for English language tasks
- Conversational AI applications
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient implementation of the Orca architecture in a lightweight 1.24B parameter format, offering various quantization options to balance between performance and resource requirements.
Q: What are the recommended use cases?
The model is best suited for agent-based instruction following tasks, conversational AI applications, and general text generation where resource efficiency is important. The Q4_K_S and Q4_K_M quantizations are recommended for most use cases.
