---

base_model: huihui-ai/Meta-Llama-3.1-8B-Instruct-abliterated

library_name: gguf

license: llama3.1

pipeline_tag: text-generation

tags:

- gguf

- quantized

- llama-3.1

- abliterated

- uncensored

- roleplay

- conversational

- pocketweights

---

⚡ PocketWeights: Llama 3.1 8B Instruct (Abliterated & Uncensored GGUF)

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.

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🧠 About This Model

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.

🎯 Key Highlights of this PocketWeights Edition

• Zero Refusals & No Preachiness: Completely removes moralizing lectures, refusal triggers, and alignment guardrails. Ideal for complex creative writing, deep roleplay, red-team security audits, and uncensored synthetic dataset generation.
• Massive 128K Context Window: Retains Meta's full Llama 3.1 architecture capabilities, allowing for book-length inputs, code repositories, and persistent long-term memory in multi-turn roleplay.
• Exceptional Efficiency: Optimized across 3-bit, 4-bit, and 6-bit quantization tiers to run with low latency on budget GPUs (4GB–8GB VRAM) and Apple Silicon Macs.

> ⚠️ Disclaimer: This model has had its safety guardrails removed. It is designed for creative writers, researchers, and developers operating in secure, local environments.

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📦 Available Files & Hardware Requirements

| 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. |

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🚀 Beginner's Quick Start Guide

You can run this model offline on your local machine in under 2 minutes:

Option 1: LM Studio (Visual GUI — Easiest)

1. Download and install LM Studio (Free for Windows, macOS, and Linux).
2. Click the Magnifying Glass (Search) icon in the left navigation bar.
3. Search for: PocketWeights/Llama-3.1-8B-Instruct-abliterated-GGUF
4. Click Download next to your preferred size (Q4_K_M is recommended), open the Chat Tab, load the model at the top, and start chatting!

Option 2: Ollama (Terminal / CLI)

If 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

Option 3: Jan / Kobold.cpp / llama.cpp / SillyTavern

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

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🤝 Support the PocketWeights Mission

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!

☕ Donation Options

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!

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📄 License & Attribution

Abliteration Source: huihui-ai

Base Architecture: Meta Llama 3.1

License: Meta Llama 3.1 Community License