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PocketWeights/Llama-3.1-8B-Instruct-abliterated-GGUF
Llama-3.1-8B-Instruct-abliterated-GGUF is a text generation model from PocketWeights. Use it when you need the model to write or continue text. It is set up for gguf. The card lists the license as llama3.1.
Heavy models, made light. PocketWeights optimizes state-of-the-art open-source LLMs into efficient GGUF formats so you can run unrestricted, powerful AI locally on consumer hardware, gaming laptops, and edge devices.
Downloads · 30 days
1.3K
43% of all-time downloads
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.gguf15.3 GB · 100%
From the Hugging Face model README
Heavy models, made light. PocketWeights optimizes state-of-the-art open-source LLMs into efficient GGUF formats so you can run unrestricted, powerful AI locally on consumer hardware, gaming laptops, and edge devices.
Meta-Llama-3.1-8B-Instruct-abliterated (created by huihui-ai) is an unaligned modification of Meta's flagship 8B model. Using orthogonal projection and activation steering, the internal refusal directions have been neutralized without destroying the model's core intelligence.
⚠️ Disclaimer: This model has had its safety guardrails removed. It is designed for creative writers, researchers, and developers operating in secure, local environments.
| Quant Format | File Size | Recommended Hardware | Best For |
|---|---|---|---|
| IQ3_M | 3.78 GB | 4 GB VRAM / 6 GB RAM | Ultra-Low Memory. Fits budget GPUs (GTX 1650, RTX 3050, 8GB System RAM) with surprising conversational coherence. |
| Q4_K_M | 4.92 GB | 6 GB VRAM / 8 GB RAM | The Balanced Standard. The community sweet spot offering full Llama 3.1 intelligence for RTX 3060, RTX 4050, and M-series Macs. |
| Q6_K | 6.60 GB | 8 GB VRAM / 16 GB RAM | Near-Lossless Quality. Maximizes reasoning accuracy, nuance, and vocabulary richness for RTX 4060, 3070, and desktop rigs. |
You can run this model offline on your local machine in under 2 minutes:
PocketWeights/Llama-3.1-8B-Instruct-abliterated-GGUFIf you already use Ollama, you can run any tier immediately with a single terminal command:
# Recommended 4-bit balanced tier
ollama run hf.co/PocketWeights/Llama-3.1-8B-Instruct-abliterated-GGUF:Q4_K_M
# Low-VRAM 3-bit tier
ollama run hf.co/PocketWeights/Llama-3.1-8B-Instruct-abliterated-GGUF:IQ3_M
# High-precision 6-bit tier
ollama run hf.co/PocketWeights/Llama-3.1-8B-Instruct-abliterated-GGUF:Q6_K
Direct Download: Go to the Files and versions tab above and download your desired .gguf file.
Load it directly into Kobold.cpp (perfect for SillyTavern roleplay), Jan.ai, Text-Generation-WebUI, or execute via llama.cpp:
llama-cli -m Llama-3.1-8B-Instruct-abliterated-GGUF-Q4_K_M.gguf -p "Write an unrestricted story about..." -ngl 33
I build, verify, and maintain automated quantization pipelines to provide high-quality, unrestricted, and hardware-friendly models to the open-source community for free.
Running conversion clusters, cloud instances, and storage requires ongoing resources. If these weights have saved you time, compute overhead, or server bills, please consider supporting the project with a small tip!
Buy me a coffee on Ko-fi: ko-fi.com/iamvishalnarayan
Web3 / Crypto (Polygon / ETH):
0x4FC189bf839A89259dd28DE8cD97883c49e15615
Tip: Sending via the Polygon network keeps transfer gas fees below $0.01!
Abliteration Source: huihui-ai
Base Architecture: Meta Llama 3.1
License: Meta Llama 3.1 Community License