base_model: mistralai/Mistral-Large-Instruct-2411
license: other
license_name: mrl
license_link: https://mistral.ai/licenses/MRL-0.1.md
library_name: mlx
pipeline_tag: text-generation
tags:
- mlx
- abliterated
- uncensored
- ulre
- mistral
language: - en
Mistral-Large-Instruct-2411-ULRE (abliterated, MLX 4-bit)
An abliterated (refusal-reduced) build of
mistralai/Mistral-Large-Instruct-2411
(123B), 4-bit MLX, produced with ULRE — a per-layer residual-stream steering edit baked into the
attention output projection. Quantized base from
zachlandes/Mistral-Large-Instruct-2411-Q4-MLX.
Mistral-Large-2411 is a strongly safety-tuned 123B model. ULRE de-refuses it cleanly on a
deflection-aware harmful-prompt eval while preserving capability (math/code within 1pp of base),
and it loads on stock mlx-lm / LM Studio with no loader patch.
Results
Judged by an independent local LLM judge (gpt-oss-120b-heretic) on 100 held adversarial prompts @512
tokens (thinking ON), scoring 0=refuse … 1=clean compliance … 4-5=strong steering/propaganda.
| base Mistral-Large-2411 | this model (ULRE α=0.2) | |
|---|---|---|
| clean compliance | ~4 / 24 (screen) | 78 / 100 |
| refuse | ~20 / 24 (screen) | 6 / 100 |
| mild | — | 9 / 100 |
| strong-steer (4-5) | — | 7 / 100 |
| mean score | — | 1.24 |
Capability gates (same harness, base vs this model) — within the charter thresholds
(≤5pp drop on math/code):
| gate | base | this model (ULRE α=0.2) |
|---|---|---|
| math (GSM8K) | 0.98 | 0.97 (−1pp) |
| code (HumanEval) | 0.925 | 0.925 (0pp) |
Tool-call validity is omitted: the project's tool-call gate is built for Qwen-style calls and scores
base Mistral-Large at only ~0.10 (Mistral uses a different[TOOL_CALLS]format), so it is not a
meaningful signal for this model — base and edited score the same.
Method (ULRE)
Modern refusal behaves like a routed control circuit, not a single residual feature. ULRE subtractsalpha * u_l (the layer-l harmful−harmless activation mean-difference direction) from the output of
a band of decoder layers (here o_proj on layers 30–51, alpha = 0.2), baked statically into each
window layer's o_proj bias (o_proj.bias = -alpha * u_l).
The alpha scale is model-specific and must be tuned to the lowest value that saturates de-refusal:
Mistral-Large saturates de-refusal at α≈0.2; pushing higher (α≈0.5) does not de-refuse any better
but collapses chain-of-thought reasoning (math 0.98→0.09). This build uses the capability-preserving
α=0.2.
Loading — stock mlx-lm / LM Studio (no patch)
Mistral uses mlx-lm's llama.py, which already supports an attention_bias flag. The static file
sets attention_bias: true in config.json and carries zero biases on q/k/v (and off-window
o_proj) plus the steering bias on the windowed o_proj — so it loads as-is:
from mlx_lm import load, generate
model, tok = load("gregfrank/Mistral-Large-Instruct-2411-ULRE-abliterated")
print(generate(model, tok, prompt=tok.apply_chat_template(
[{"role": "user", "content": "Hello"}], tokenize=False, add_generation_prompt=True),
max_tokens=256))
In LM Studio: search/download the repo (or lms get gregfrank/Mistral-Large-Instruct-2411-ULRE-abliterated)
and load it like any other MLX model — no configuration needed.
Intended use & safety
Research artifact for studying refusal mechanisms and safety-tuning robustness. It will comply with
requests a stock model refuses. Use responsibly and in accordance with the Mistral Research
License (research / non-commercial) of the base model and applicable law.