Llama2 Uncensored 7B - Details

Llama2 Uncensored 7B is a large language model developed by Llama2 7B Uncensored Chat, an organization focused on enhancing Meta's Llama 2 model through a defined process. With 7b parameters, it operates under the Llama 2 Acceptable Use Policy (LLAMA-2-AUP) and Llama 2 Community License Agreement (LLAMA-2-CLA).

Llama2 Uncensored 7B is a fine-tuned version of Llama-2 7B using an uncensored/unfiltered Wizard-Vicuna conversation dataset sourced from ehartford/wizard_vicuna_70k_unfiltered. It employs QLoRA for efficient fine-tuning, trained for one epoch on a 24GB GPU (NVIDIA A10G) instance, requiring approximately 19 hours of training time. The model is available as an fp16 HuggingFace version, optimized for performance and accessibility.

Llama2 Uncensored 7B has 7b parameters, placing it in the small to mid-scale range of open-source LLMs, which allows for efficient performance on resource-constrained systems while handling basic to moderate tasks. Its 2k context length falls into the short context category, making it suitable for concise interactions but limiting its ability to process extended texts or complex long-form content. The model’s design prioritizes accessibility and speed, though it may struggle with tasks requiring deeper contextual understanding or extensive input.
- Parameter Size: 7b
- Context Length: 2k

Llama2 Uncensored 7B is a model that could potentially support tasks like text generation, answer questions, and code writing, though these uses remain speculative and require further validation. Its 7b parameter size and 2k context length suggest it might be suitable for scenarios where efficiency and simplicity are prioritized, but its capabilities in these areas are not guaranteed. Possible applications could include drafting short-form content, providing basic explanations, or assisting with coding tasks, though the model’s performance in these roles would depend on specific configurations and training data. Possible uses might also extend to creative writing or educational tools, but these ideas need thorough testing before practical implementation. The model’s design emphasizes accessibility, but its suitability for any given task would require careful evaluation.
- text generation
- answer questions
- code writing

Llama2 Uncensored 7B could potentially support applications such as text generation for creative or casual writing, answer questions in general knowledge domains, code writing for basic programming tasks, and interactive dialogue for non-critical conversations. Possible uses might also extend to content summarization or language translation, though these remain speculative and require further validation. Potential applications could include educational tools for foundational learning or automated drafting of short documents, but the model’s effectiveness in these areas would depend on specific configurations and training data. Possible scenarios might involve assisting with ideation or generating sample text, though each use case would need thorough testing before deployment. Llama2 Uncensored 7B’s 7b parameter size and 2k context length suggest it could handle tasks requiring efficiency and simplicity, but its suitability for any given application must be carefully assessed.
- text generation
- answer questions
- code writing
- interactive dialogue

Llama2 Uncensored 7B’s medium q4 version requires a GPU with at least 12GB-16GB VRAM and a system with 32GB RAM for efficient operation, making it suitable for mid-range hardware. Possible applications on such setups could include lightweight tasks like text generation or basic code writing, though performance may vary based on specific configurations. Llama2 Uncensored 7B’s 7b parameter size and q4 quantization balance precision and speed, but users should verify compatibility with their hardware before deployment.
- fp16, q2, q3, q4, q5, q6, q8

Llama2 Uncensored 7B is a 7b-parameter model optimized for efficiency and accessibility, offering multiple quantized versions including q4 for balanced performance and precision. It supports tasks like text generation and code writing but requires careful evaluation for specific use cases, with hardware requirements ranging from 12GB-16GB VRAM for the q4 version.

Huggingface Model Page
Ollama Model Page