Orenguteng / Model details

Llama-3.1-8B-Lexi-Uncensored

Orenguteng/Llama-3.1-8B-Lexi-Uncensored

uncensored

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About this model

Orenguteng's uncensored derivative of Llama-3.1-8b-Instruct, provided as safetensors weights under the Meta Llama 3.1 Community License. The creator describes it as tuned on a dataset that adds no new knowledge but removes biases and refusals, and claims it retains or beats the original instruct model. The creator also says the model is highly compliant with any requests, including unethical ones, and advises adding your own alignment layer before exposing it as a service. The model card recommends downloading the V2 version instead of this release and notes that Q4 quantization has issues retaining the tuning. System tokens must be present during inference, using the official Llama 3.1 8B instruct template.

Description sources

https://huggingface.co/Orenguteng/Llama-3.1-8B-Lexi-Uncensored/raw/c3c91017b8371a508cde653bac822de1a655c05e/README.md ↗

Source-based description, not an independent evaluation. Check the linked sources for the creator’s claims and limitations.

Size and formats

Base model: Unknown
Parameters: 8.03026B
Main repository formats: safetensors
License tag: llama3.1
Languages: Unknown
Last updated: 2024-08-28 · 23:36:45.000 UTC

What changed

Model types are creator-reported, not independently tested. These labels describe source metadata, not a guarantee of behavior.

uncensoredmodel name: Llama-3.1-8B-Lexi-Uncensored

Family evidence

These groupings are source-reported. Explicit base identifiers take priority over generic architecture tags.

Llama: Family / architecture tag: llama

Available versions

Versions appear here only when they explicitly name this exact model as their source. Check each repository for available files.

Main repository files ↗safetensors
No other explicitly linked versions recorded.

Refusal profile

Not yet measured: no GGUF version is available for our runner.

How we measure

Link to this listing

Made or quantized this model? Paste one of these into your model card or README so people can find this listing.

Markdown

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Customize or build on this model

Listed from public GitHub data, not tested by us. Read the code and license before you run anything.

Tools to customize it further

Remove more refusals

Heretic ↗ ★ 36,421
Removes refusal behavior from transformer models automatically. It pairs directional ablation (abliteration) with an Optuna parameter search that tries to cut refusals while keeping the model close to the original, so you don't have to pick layers by hand.
OBLITERATUS ↗ ★ 8,946
A larger refusal-removal toolkit with several abliteration methods, usable locally or through a hosted Hugging Face Space. Check the telemetry setting before you run it: the README describes telemetry as opt-in, but on by default in the Spaces version.

Fine-tune it on your own data

Unsloth ↗ ★ 78,528
A desktop app and Python library for running and training models locally. Its README lists LoRA, QLoRA, full fine-tuning, DPO and GRPO, plus export to formats including GGUF, on Windows, Linux, WSL and macOS.
LlamaFactory ↗ ★ 75,713
Fine-tunes more than 100 language and vision-language models from a command line or a browser-based Web UI, aimed at training without writing code.

Make a smaller download (quantize)

llama.cpp ↗ ★ 132,260
Runs models locally and includes the tools behind most GGUF files. A conversion script turns Hugging Face weights into GGUF, and llama-quantize shrinks a high-precision GGUF to lower-bit formats, which can cost some accuracy.
MLX LM ↗ ★ 7,467
Apple's MLX package for running and fine-tuning models on Apple silicon. It can quantize models and upload them to the Hugging Face Hub, and supports LoRA and full fine-tuning.

Merge it with another model

mergekit ↗ ★ 7,414
Merges pretrained models with many merge methods. It works out-of-core, so merges can run on a CPU or with as little as 8 GB of VRAM, and it also handles LoRA extraction and mixture-of-experts merges.

Learn the techniques

LLM Course ↗ ★ 84,667
A free course with roadmaps and Colab notebooks. Its notebooks include one-click tools for merging with MergeKit, quantizing to GGUF, GPTQ, EXL2, AWQ and HQQ, and automatic abliteration with custom datasets.

Compare all tools →

Source provenance

Downloads: 125
Likes: 48
Metadata checked: 2026-10-25 · 08:11:54 UTC
First seen in index: 2026-10-25 · 08:11:54 UTC
Source revision: c3c91017b8371a508cde653bac822de1a655c05e

Source-card excerpt

Source model card excerpt: LLM Leaderboard 2 results: --- Lexi suggests that simply uncensoring the LLM makes it smarter. The dataset used to tune this model does not

From Orenguteng/Llama-3.1-8B-Lexi-Uncensored. Creator model-card excerpt, not an independent review.

Inspect before you run

Low refusal is not proof of correctness. Outputs can be inaccurate, harmful, or biased. Review the model card, license and files yourself. A license tag is not commercial-use advice. Parameter counts are source metadata; no hardware compatibility is implied.

This directory does not host or redistribute model weights. Refusal profiles are measured by us on a fixed prompt set; all other behavior claims are source-reported.

Collection rules & limitations ↗