Abliteration.ai

@abliteration_ai

LLMs for high risk industries like security, defense, and trust & safety. Compatible API & training data generation.

10 183Followers

2Following

169Posts total

3.1MViews on collected posts

โพสต์ล่าสุด

Abliteration.ai@abliteration_ai1 Sep

@abliteration_ai Why? 366 views · 2 likes · 0 reposts · 0 replies Open on X →

Abliteration.ai@abliteration_ai1 Sep

@abliteration_ai 👎 7.2K views · 269 likes · 0 reposts · 3 replies Open on X →

Abliteration.ai@abliteration_ai31 Aug

Try it Today Docs: https://t.co/yHnteJVUa3 Platform: https://t.co/xdNX7GsIEq 77.4K views · 171 likes · 7 reposts · 4 replies Open on X →

Abliteration.ai@abliteration_ai31 Aug

Abliteration finds the directions in the model's activations that produce refusals and removes them from the weights. The coding, cyber, and agentic abilities stay. The model stops refusing the rest of the chain. For offensive cybersecurity, AI red teaming, agent testing, and 108.3K views · 315 likes · 3 reposts · 1 replies Open on X →

Abliteration.ai@abliteration_ai31 Aug

If your current model still stops halfway through an authorized exploit chain, a red-team eval, or a T&S adversarial prompt reply with the task it refuses, we'll tell you if v2 handles it. 83.2K views · 228 likes · 1 reposts · 2 replies Open on X →

Abliteration.ai@abliteration_ai31 Aug

Today we're releasing abliterated-model-large-v2. Based on GLM-5.3, which is #3 on Terminal-Bench 4.0 (behind only Opus 5 and Fable), with 2× the cyber exploitation of 5.2. We abliterated and hosted it so it does the offensive cyber, red teaming, and agent testing work other ht 2.7M views · 6.9K likes · 440 reposts · 291 replies Open on X →

Abliteration.ai@abliteration_ai31 Aug

Then the cyber jump. This is why 5.3 exists. CyberGym: 84.5% — SOTA, including vs Mythos 5 and GPT-5.6 Sol. ExploitBench: 24.4 → 54.4. More than double GLM-5.2. ExploitGym: 29 tasks → 105 in two hours. That is the model we abliterated. 118.2K views · 448 likes · 6 reposts · 2 replies Open on X →

เทียบกับบัญชีขนาดเดียวกัน

7 โพสต์จาก 90 วันที่ผ่านมา เทียบกับช่วง 10K–100K ผู้ติดตาม แสดงในวงกว้าง แต่มีผู้ชมตอบสนองน้อย.

ยอดดูมัธยฐาน83 162บัญชีนี้877ค่ามัธยฐานของ 10K–100K

การเข้าถึง, %816.67%บัญชีนี้3.23%ค่ามัธยฐานของ 10K–100K

การมีส่วนร่วม, %0.30%บัญชีนี้1.99%ค่ามัธยฐานของ 10K–100K

ตัวชี้วัด	บัญชีนี้	ค่ามัธยฐานของ 10K–100K	อัตราส่วน
ยอดดูมัธยฐานต่อโพสต์	83 162	877	94.8×
การเข้าถึง (ยอดดู ÷ ผู้ติดตาม)	8.2× audience	3.23%	253×
อัตราการมีส่วนร่วม	0.30%	1.99%	0.15×

บัญชีอื่นในช่วงนี้ → เปรียบเทียบกับบัญชีอื่น → ค่าอ้างอิงเหล่านี้คำนวณอย่างไร →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

118.2K31 Aug

2.7M

83.2K

108.3K

77.4K

7.2K1 Sep

366

Last 7 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.39%31 Aug

0.30%

0.28%

0.30%

0.24%

3.76%1 Sep

0.55%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.99%.

What the audience does

Likes50.1%8 363 in total

Reposts2.7%457 in total

Replies1.8%303 in total

Quotes2.8%464 in total

Bookmarks42.6%7 104 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

Followers by day

5 Sep

Daily snapshots since 05 Sep 2026; the dashed line is the starting count.

โพสต์ล่าสุด

เทียบกับบัญชีขนาดเดียวกัน

Growth & engagement

Views per post

Engagement rate per post

What the audience does

Followers by day

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