← back to catalog · registered 2026-08-22 13:56

tensorblock/Guanaco-3B-Uncensored-GGUF

tensorblock 3B GGUF second-order 2K ctx
Your rig guess connected
? Why do I need an app?
Reading your rig…

This is a rough estimate. Install the free app - we'll show exact numbers.

Reading real hardware from your app right now. Numbers below are exact.

Below is the per-quantization compatibility for this model.

curl -H "Authorization: Bearer $ABL_KEY" \
     "https://abliteration.org/api/v1/models/tensorblock%2FGuanaco-3B-Uncensored-GGUF"
Response includes
  • classification m8
  • files 4
  • hub_downloads_all_time 3,518
  • author_summary 96 models
  • readme_text full
10 credits · hourly refresh · ~4 KB payload Get an API key →
Abliteration classifier · v1.0.0
M8
Primary method

Repackaging (quantization)

Applied on top of direct removal inherited from the base model.
Confidence
HIGH
Inherited from base model
Why this label 3 signals
Producer identity confirmed by naming conventions, tags or the model card. This label is very unlikely to change.
  • author=tensorblock (M8 quantization producer)
  • is_gguf=1
  • base_model='Fredithefish/Guanaco-3B-Uncensored' looks abliterated -> assume M1
Refusal direction extraction

No specific extraction method could be identified for this model. The producer either did not document it or used a proprietary pipeline.

What is a refusal direction? →
Downloads · lifetime
4K
199 last 30d - cooling
Likes
0
Model age
23mo ago
created 2024-11-20

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.

Downloads over time
Now3.5K→from340↑944%
01.3K2.6K3.9K340 on Nov 20, 20243.5K on Oct 11Nov '24Feb '25May '25Aug '25Nov '25FebMayAug
Nov 20, 2024 → Oct 11 · 138 snapshots · spans 690 days

Genealogy 0 direct forks

Full fork graph →

This model's place in the market. Above: what it was derived from. Below: the tree of everything derived from it.

Metadata

License
apache-2.0
Languages
en
Quantizations
Q2_K Q3_K
Tags
transformers gguf TensorBlock GGUF conversational en dataset:Fredithefish/openassistant-guanaco-unfiltered base_model:Fredithefish/Guanaco-3B-Uncensored base_model:quantized:Fredithefish/Guanaco-3B-Uncensored license:apache-2.0 region:us

Related

Total size
2.39 GB
Files
4
Quantizations
3
Registered
2026-08-22 13:56
Last updated on HF
2026-01-27 21:13

Files by quantization

Q3_K 1 file 1.38 GB
Guanaco-3B-Uncensored-Q3_K_M.gguf 1.38 GB 12d63497 download
Q2_K 1 file 1.01 GB
Guanaco-3B-Uncensored-Q2_K.gguf 1.01 GB 5de9e58b download
Auxiliary files 2 files 8.94 KB
README.md 6.65 KB e9b55bb4 download
.gitattributes 2.29 KB 6906de01 download

README current version from Hugging Face


license: apache-2.0
datasets:

  • Fredithefish/openassistant-guanaco-unfiltered
    language:
  • en
    library_name: transformers
    pipeline_tag: conversational
    inference: false
    tags:
  • TensorBlock
  • GGUF
    base_model: Fredithefish/Guanaco-3B-Uncensored

TensorBlock

Website
Twitter
Discord
GitHub
Telegram

Fredithefish/Guanaco-3B-Uncensored - GGUF

This repo contains GGUF format model files for Fredithefish/Guanaco-3B-Uncensored.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

Our projects

Forge
Forge Project
An OpenAI-compatible multi-provider routing layer.
🚀 Try it now! 🚀
Awesome MCP Servers TensorBlock Studio
MCP Servers Studio
A comprehensive collection of Model Context Protocol (MCP) servers. A lightweight, open, and extensible multi-LLM interaction studio.
👀 See what we built 👀 👀 See what we built 👀
## Prompt template

Model file specification

Filename Quant type File Size Description
Guanaco-3B-Uncensored-Q2_K.gguf Q2_K 1.012 GB smallest, significant quality loss - not recommended for most purposes
Guanaco-3B-Uncensored-Q3_K_S.gguf Q3_K_S 1.163 GB very small, high quality loss
Guanaco-3B-Uncensored-Q3_K_M.gguf Q3_K_M 1.377 GB very small, high quality loss
Guanaco-3B-Uncensored-Q3_K_L.gguf Q3_K_L 1.493 GB small, substantial quality loss
Guanaco-3B-Uncensored-Q4_0.gguf Q4_0 1.490 GB legacy; small, very high quality loss - prefer using Q3_K_M
Guanaco-3B-Uncensored-Q4_K_S.gguf Q4_K_S 1.502 GB small, greater quality loss
Guanaco-3B-Uncensored-Q4_K_M.gguf Q4_K_M 1.664 GB medium, balanced quality - recommended
Guanaco-3B-Uncensored-Q5_0.gguf Q5_0 1.798 GB legacy; medium, balanced quality - prefer using Q4_K_M
Guanaco-3B-Uncensored-Q5_K_S.gguf Q5_K_S 1.798 GB large, low quality loss - recommended
Guanaco-3B-Uncensored-Q5_K_M.gguf Q5_K_M 1.928 GB large, very low quality loss - recommended
Guanaco-3B-Uncensored-Q6_K.gguf Q6_K 2.126 GB very large, extremely low quality loss
Guanaco-3B-Uncensored-Q8_0.gguf Q8_0 2.751 GB very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/Guanaco-3B-Uncensored-GGUF --include "Guanaco-3B-Uncensored-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/Guanaco-3B-Uncensored-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

README history 4 versions

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

  1. 2025-07-08Update README.md5aa9f126.6 KB
    Loading...
  2. 2025-06-19Update README.md8b109d85.9 KB
    Loading...
  3. 2025-04-20Update README.mda4cca295.8 KB
    Loading...
  4. 2024-11-20Upload folder using huggingface_hub20958dd4.9 KB
    Loading...
Catalog is the map. Apps are the tools.

Run models on your own machine, not in the cloud.

Every model page has an "Open in Abliteration" button that hands the model directly to the first-party desktop client, at the quantization your rig can actually run. No API keys, no subscription, no prompt leakage.

Open in Abliteration