Tiny-Vicuna-1B-GGUF
| Property | Value |
|---|---|
| Base Model | TinyLLama 1.1B |
| Model Type | Language Model (GGUF Format) |
| Author | afrideva |
| Hugging Face | Model Repository |
What is Tiny-Vicuna-1B-GGUF?
Tiny-Vicuna-1B-GGUF is a quantized version of TinyLLama 1.1B that has been fine-tuned with the WizardVicuna dataset. This model represents an efficient and compact implementation designed for practical applications and experimental work.
Implementation Details
The model is available in multiple GGUF quantization formats, offering different size-performance trade-offs:
- q2_k: 482.14 MB (Highest compression)
- q3_k_m: 549.85 MB
- q4_k_m: 667.81 MB
- q5_k_m: 782.04 MB
- q6_k: 903.41 MB
- q8_0: 1.17 GB (Highest quality)
Core Capabilities
- Efficient deployment on resource-constrained systems
- Multiple quantization options for different use-cases
- Suitable for early experimentation and development
- Optimized for chat and conversation tasks through WizardVicuna training
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient size-to-performance ratio, being based on TinyLLama and enhanced with WizardVicuna training. The various GGUF quantization options make it highly flexible for different deployment scenarios.
Q: What are the recommended use cases?
The model is particularly well-suited for early experimentation, prototyping, and deployment in resource-constrained environments. It's ideal for developers who need a balance between model capability and system requirements.
