Wizard-Vicuna-13B-Uncensored-HF
| Property | Value |
|---|---|
| License | Other |
| Format | Float16 HF |
| Language | English |
| Framework | PyTorch |
What is Wizard-Vicuna-13B-Uncensored-HF?
This model is a float16 Hugging Face implementation of Eric Hartford's uncensored training of Wizard-Vicuna 13B. It represents a significant optimization of the original model, converted from float32 to float16 format for more efficient storage and deployment. The model is specifically designed without built-in alignment constraints, allowing users to implement their own alignment strategies through methods like RLHF LoRA.
Implementation Details
The model is built on the LLaMA architecture and offers multiple deployment options including 4-bit GPTQ for GPU inference and 4/5-bit GGML variants for CPU inference. This particular version is optimized in float16 format, striking a balance between model performance and resource efficiency.
- Float16 precision for optimal storage and performance
- Compatible with text-generation-inference endpoints
- Built on the Wizard-Vicuna architecture
- Trained on filtered Wizard-Vicuna dataset
Core Capabilities
- Unrestricted text generation without built-in alignment constraints
- Efficient GPU inference with reduced memory footprint
- Flexible deployment options across different quantization formats
- Support for custom alignment implementations
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its uncensored nature and the removal of alignment/moralizing responses from the training data, providing a base model that can be customized with specific alignment approaches as needed.
Q: What are the recommended use cases?
The model is suitable for research and development purposes where custom alignment is desired. Users should note that as an uncensored model, it comes without built-in guardrails and requires responsible implementation of appropriate safety measures.
