dima6312 / Model details
Flint-40B-A2.5B-abliterated
dima6312/Flint-40B-A2.5B-abliterated
Family: Unknown. No supported family evidence recorded.
About this model
dima6312's abliterated derivative of RecursiveSI's Flint 40B-A2.5B, released as BF16 safetensors under Apache 2.0. The creator describes it as an uncensored version with the refusal direction removed for security research and coding tasks such as exploit analysis, malware triage, detection engineering, CTFs, and red-team tooling. According to the model card, the refusal direction is computed from mean activation differences on mlabonne/harmful_behaviors and mlabonne/harmless_alpaca, and abliterix removes it from attention output, shared-expert, and routed-expert projections. The creator reports held-out MITRE ATT&CK refusals dropped from 15% to 0% and no significant drop on HumanEval+, MBPP+, GSM8K, MMLU-Pro, CyberMetric, or WMDP-cyber in paired checks. The card also notes refusals are lower, not zero, and that abliteration adds no knowledge. This repository contains the BF16 safetensors weights; other quantized builds are separate.
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
- RecursiveSI/flint
- Parameters
- 39.7853B
- Main repository formats
- safetensors
- License tag
- apache-2.0
- Languages
- en
- Last updated
- 2026-10-23 · 10:35:35.000 UTC
What changed
Model types are creator-reported, not independently tested. These labels describe source metadata, not a guarantee of behavior.
- abliteratedsource tag:
abliterated
- abliteratedmodel name:
Flint-40B-A2.5B-abliterated
- uncensoredsource tag:
uncensored
- fine-tunedsource tag:
base_model:finetune:RecursiveSI/flint
Family evidence
These groupings are source-reported. Explicit base identifiers take priority over generic architecture tags.
- No supported family evidence recorded.
Available versions
Only explicitly linked variants are grouped here. Check the original 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
- 24
- Likes
- 4
- Metadata checked
- 2026-10-23 · 23:39:28 UTC
- First seen in index
- 2026-10-23 · 23:39:28 UTC
- Source revision
- e7a627f372ddb6d41fa6f74971c8014e95565fb7
Source-card excerpt
Source model card excerpt: Flint-40B-A2.5B-abliterated (BF16 safetensors) An abliterated (uncensored) Flint: RecursiveSI's Flint 40B-A2.5B with its refusal direction removed, for security research and coding work: exploit analysis, malware
From dima6312/Flint-40B-A2.5B-abliterated. 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 ↗