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jetro30087/vicuna-Wizard-7B-Uncensored-q3f16_0

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  • classification m-uncensored
  • files 4
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Abliteration classifier · v1.0.0
M-U
Primary method

Uncensored (method unknown)

No other method signals detected in this model.
Confidence
LOW
Why this label 3 signals
Weak or ambiguous signals. Best guess based on catalog patterns; treat as tentative and check the evidence below.
  • 'uncensored' in name/tags but no 'abliterated' marker
  • method not identifiable from author declaration alone
  • may be DPO fine-tune, prompt engineering, or unknown technique
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No specific extraction method could be identified for this model. The producer either did not document it or used a proprietary pipeline.

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Downloads · 30-day
0
Likes
2
Model age
3.3y ago
created 2023-06-15

Training datasets

Corpora the author lists in the model card. Datasets tracked in our /datasets catalog carry a category badge linking to the workflow stage. Others open on Hugging Face.

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Metadata

License
other
Languages
en
Tags
text-generation en dataset:ehartford/wizard_vicuna_70k_unfiltered license:other region:us

Related

Total size
0 B
Files
4
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2023-06-15 23:52

Files by quantization

Auxiliary files 4 files 50.4 MB
mod_cache_before_build_vulkan.pkl 32.6 MB a6007ff1 download
vicuna-Wizard-7B-Uncensored-q3f16_0-vulkan.dll 17.8 MB 975135ef download
README.md 3.07 KB 75830972 download
.gitattributes 1.64 KB f511730e download

README current version from Hugging Face


license: other
datasets:

  • ehartford/wizard_vicuna_70k_unfiltered
    language:
  • en
    pipeline_tag: text-generation

This is the PC version of this model for AMD/NVIDA GPUs.

Linux Version here (https://huggingface.co/jetro30087/vicuna-Wizard-7B-Uncensored-linux-q4f16_0)

Android Version here - (https://huggingface.co/jetro30087/vicuna-Wizard-7B-Uncensored-linux-q3f16_0/settings)

Model Card for vicuna-Wizard-7B-Uncensored-q3f16_0
Model Description
This Language Model (vicuna-Wizard-7B-Uncensored-q3f16_0) is based on Facebook's "Llama" 7B parameter model, trained on the Wizard-Vicuna uncensored dataset under a non-commercial license. It was specifically developed and formatted for use within the MLC-LLM project, which you can find more details about at MLC-LLM project (https://github.com/mlc-ai/mlc-llm).

The model is designed for research and general text generation purposes. Thanks to MLC-LLM's Vulkan compatibility, the model is capable of working on both Nvidia and AMD graphics cards.

Model Usage
The vicuna-Wizard-7B-Uncensored-q3f16_0 model can generate human-like text that's useful for a variety of purposes, including but not limited to research, chatbots, writing aids, and more. You can use the model through MLC-LLM chat by copying it to the mlc-chat/dist folder of a compile MLC-Chat client.

Limitations and Bias
Although the model is capable of generating high-quality text, it is important to note that it is not perfect. Here are some potential limitations and biases:

Output quality: Although trained on a large dataset, the model may occasionally produce text that is nonsensical or does not align with the input prompt.

Biases in the data: The model has been trained on the Wizard-Vicuna uncensored dataset, and as such, it may have inherited biases present in this data.

Safety and content: The uncensored nature of the training dataset means that the model could potentially produce text that some people find offensive, inappropriate, or politically biased. We recommend using this model with care, especially in environments with young users or those who might be affected by such content.

Incorrect information: The model generates text based on patterns it learned during training and does not have access to real-world knowledge or updates beyond its training cut-off. As a result, the information it provides should always be verified for accuracy.

Ethical Considerations and Safety
While using this model, consider the following:

Always verify the information provided by the model with reliable external sources before using it to make decisions or for factual reference.
Monitor the output of the model for any potentially inappropriate or harmful content, especially if it is being used in a public or sensitive setting.
Keep in mind the potential biases inherited from the training data and account for these when interpreting the output.
Disclaimer
This model is provided as-is, and the developers make no warranties regarding its performance, appropriateness, or accuracy. Use it at your own risk.
license: othertions](https://mlc.ai/mlc-llm/docs/tutorials/runtime/cpp.html) for details.

README history 14 versions

The author's README evolved over time. Click a version to see its content at that point.

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  14. 2023-06-15initial commit7e8e70f23 B
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