LLaMA2 70B Chat Uncensored
| Property | Value |
|---|---|
| Base Model | LLaMA2 70B |
| Training Method | QLoRA Fine-tuning |
| License | LLaMA2 |
| Research Paper | QLoRA Paper |
| Training Dataset | wizard_vicuna_70k_unfiltered |
What is llama2_70b_chat_uncensored?
llama2_70b_chat_uncensored is a fine-tuned variant of the LLaMA2 70B model that prioritizes direct, unfiltered responses. The model was trained using QLoRA technology on an uncensored Wizard-Vicuna conversation dataset, requiring approximately one week of training on an NVIDIA A100 80GB GPU.
Implementation Details
The model leverages QLoRA for efficient fine-tuning, utilizing 8-bit quantization that consumes 67.2GB of GPU memory, or 4-bit quantization using 40.8GB. The training process involved three epochs and employed specific prompt formatting for consistent interactions.
- Memory Requirements: 500GB RAM/Swap for model merging
- Available in fp16, GGML, and GPTQ versions
- Implements Wizard-Vicuna conversation format
Core Capabilities
- Direct, straightforward responses without safety filters
- Maintains factual accuracy while avoiding moralistic intervention
- Supports context window of 4096 tokens
- Efficient deployment options with various quantization levels
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its approach to AI responses, removing the typical safety filters and moral interventions found in standard LLaMA2 chat models. It provides direct, factual responses without attempting to modify user behavior or language.
Q: What are the recommended use cases?
The model is suited for applications requiring straightforward, unfiltered responses while maintaining factual accuracy. It's particularly useful in scenarios where direct communication is preferred over safety-filtered interactions, though users should apply their own ethical judgment.
