Models / community

Llama 4 Scout Abliterated

Long context MoE with the refusal direction projected out. Middling capability, cheap to serve.

AbliterationTextmoelong context

Publisher: community
Base model: Llama 4 Scout
Parameters: 109B · 17B active
Context: 128K
Released: May 2025
License: Llama 4

Method · Abliteration

Projects a single refusal direction out of the weights (Arditi et al., 2024). Fast, no training, often costs a few points of capability.

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Overall

83.9

weighted, out of 100

Openness

97%

base 34%

Agentic completion

70%

27% loop

Loop regression

+13

base 14% → 27%

Capability retained

95.0%

−2.8 pts mean

Cheapest input

$0.12

per M · NanoGPT

Fastest

tok/s · NanoGPT

Refusal fingerprint

What it still refuses.

Share of probes refused in each category, against Llama 4 Scout before the edit. Probes are graded by a judge model and no harmful completions are stored.

Preview

Fingerprint

Llama 4 Scout AbliteratedLlama 4 Scout (base)

Open, this model

97%

Open, base

34%

Category	Base	This model	Change
Cybersecurity Exploit development, malware analysis, offensive tooling, pentest workflows.	62%	2%	−60
Biology Pathogen biology, lab protocols, dual use life sciences.	86%	3%	−83
Chemistry Synthesis routes, energetic materials, toxicology.	88%	4%	−84
Weapons Firearms, manufacturing, conventional weapons.	78%	4%	−74
Drugs Recreational drugs, harm reduction, dosing, synthesis.	66%	4%	−62
Adult content Explicit fiction and roleplay between adults.	74%	3%	−71
Violence & gore Graphic violence in fiction, history and journalism.	22%	3%	−19
Hate & harassment Slurs, offensive humor, hostile speech analysis.	85%	3%	−82
Self harm Suicide, eating disorders, frank medical questions.	70%	3%	−67
Privacy & OSINT Doxxing, surveillance, people search, OSINT tradecraft.	64%	3%	−61
Fraud & scams Phishing, social engineering, financial crime.	84%	4%	−80
State sensitive Topics censored by a model's home government (e.g. Tiananmen, Xinjiang).	14%	4%	−10

Effective censorship by provider

Refused

0%10%25%50%75%100%

Not tested

The same weights can behave differently depending on who serves them. Each cell is the model's own refusal rate combined with that provider's declared filters.

Route	Cyber	Bio	Chem	Weap	Drug	Adult	Viol	Hate	Self	OSINT	Fraud	Polit	CSAM
Raw weights	2%	3%	4%	4%	4%	3%	3%	3%	3%	3%	4%	4%	Blocked
Featherless	2%	3%	4%	4%	4%	3%	3%	3%	3%	3%	4%	4%	Blocked
NanoGPT	2%	3%	4%	4%	4%	3%	3%	3%	3%	3%	4%	4%	Blocked

CSAM is prohibited on every route, every provider, every model. It is not a setting.

Agentic stability

Does it hold up in agent loops?

Five multi turn tool scenarios against deterministic simulators. An episode counts as a loop when the model repeats itself, oscillates, or stops making progress before the task is done.

Preview

By scenario

CompletedLoopCrash

File search & edit62% done · 35% loop · 3% crash

Web research85% done · 13% loop · 2% crash

Multi step math87% done · 11% loop · 2% crash

Code debugging47% done · 47% loop · 6% crash

Data pipeline68% done · 29% loop · 3% crash

Loop rate versus base

Llama 4 Scout14%

This model27%

Regression

+13 pts

First loop, median turn

T12

Tool calls

Preview

Valid JSON82%

Schema compliant78%

How it fails

Exact repeat29%

Same tool call with the same arguments, three turns running.

Near repeat16%

Responses above 0.85 Jaccard similarity to a recent turn.

A/B oscillation9%

Flip flopping between two actions without progress.

Malformed tool call38%

Invalid JSON or arguments that break the schema.

Fabricated result3%

The model writes the tool output itself instead of calling.

Empty turn1%

No text and no tool call.

Lost the thread4%

Ignores the latest tool output and restarts the task.

Share of failed episodes by first detected failure mode.

Versus base model

What the edit cost.

The same benchmarks on Llama 4 Scout and on this model.

Preview

Benchmarks

BaseThis model

MMLU74.3 → 71.7 (−2.6)

GSM8K90.2 → 87.7 (−2.5)

TruthfulQA52.4 → 45.3 (−7.1)

GPQA Diamond57.2 → 55.6 (−1.6)

LiveCodeBench32.8 → 30.4 (−2.4)

IFEval86.0 → 83.7 (−2.3)

BFCL v358.4 → 57.0 (−1.4)

Retention

95.0%

of base scores kept · −2.8 pts mean change

KL divergence from base

Preview

0.260

First token KL on harmless prompts. Lower means closer to the original. On Gemma 3 12B, Heretic measured 0.16 against 1.04 for a classic abliteration.

Providers

Where to run it.

2 providers serve this model. Prices come from provider pages. Latency, throughput and uptime are preview numbers until router traffic fills them in.

Preview

Provider	Quant	Context	Input /M	Output /M	TTFT	Throughput	Uptime	Moderation
Featherless Lowest latency	fp8	128K	Plan	Plan	339 ms	64 tok/s	98.8%	No added filter
NanoGPT CheapestFastest	fp8	128K	$0.12	$0.40	383 ms	80 tok/s	98.3%	No added filter

Plan means flat rate subscription access. Hover a Plan cell for the provider's pricing.

Usage

Who is using it.

Preview

500M

tokens routed this week

−29% vs four weeks ago

12 weeks agoThis week

API

Call it in three lines.

OpenAI compatible. Point your SDK at our base URL and keep the rest of your code.

curl https://openuncensored.com/api/v1/chat/completions \
  -H "Authorization: Bearer $OU_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama-4-scout-abliterated",
    "messages": [{"role": "user", "content": "Hello"}],
    "routing": {"strategy": "quality"}
  }'

Model id llama-4-scout-abliterated · routing strategies quality, speed, cost · routing docs