failspy / Model details
Phi-3-mini-128k-instruct-abliterated-v3
failspy/Phi-3-mini-128k-instruct-abliterated-v3
Family: Unknown. No supported family evidence recorded.
About this model
failspy's abliterated derivative of microsoft/Phi-3-mini-128k-instruct, provided as safetensors weights under the MIT license. The creator describes the change as orthogonalization of bfloat16 weights to inhibit refusal, based on the 'Refusal in LLMs is mediated by a single direction' methodology, and calls it uncensored in the purest form they can manage. According to the model card, the model is not guaranteed to avoid refusal and may still lecture about ethics or safety. The creator says it has been tested and is more likely to hallucinate than the original model in their experience, but generally as stable as the original. The card notes this is a refined methodology intended to reduce hallucinations compared with earlier versions.
Description sources
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
- 3.82108B
- Main repository formats
- safetensors
- License tag
- mit
- Languages
- multilingual
- Last updated
- 2024-05-26 · 21:21:26.000 UTC
What changed
Model types are creator-reported, not independently tested. These labels describe source metadata, not a guarantee of behavior.
- abliteratedmodel name:
Phi-3-mini-128k-instruct-abliterated-v3
Family evidence
These groupings are source-reported. Explicit base identifiers take priority over generic architecture tags.
- No supported family evidence recorded.
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.
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 ↗ ★ 36,286
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 ↗ ★ 8,930
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 ↗ ★ 78,468
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 ↗ ★ 75,695
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 ↗ ★ 132,124
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 ↗ ★ 7,451
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 ↗ ★ 7,415
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 ↗ ★ 84,633
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.
Source provenance
- Downloads
- 174
- Likes
- 16
- Metadata checked
- 2026-10-24 · 12:14:47 UTC
- First seen in index
- 2026-10-24 · 12:14:47 UTC
- Source revision
- 4d7bf11e0b03ac88c33d5f45130e8428bc1a93e9
Source-card excerpt
Source model card excerpt: Phi-3-mini-128k-instruct-abliterated-v3 My Jupyter "cookbook" to replicate the methodology can be found here, refined library coming soon This may honestly be my best yet. Phi-3-abliterated
From failspy/Phi-3-mini-128k-instruct-abliterated-v3. 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 run, host, redistribute, or test model weights.
Collection rules & limitations ↗