glm-5.3-abliterated
GLM 5.3 abliterated or uncensored API access with RouterHub. Browse 35 jailbreak prompt links, compare GLM models, and find SDK examples, coding-tool setup, pricing, and evaluation guides.
Runs with mockssource: GitHubhomepagePythonMITcommit 06c2abcaef9d
Python, MIT licensed. The project labels itself: abliterated, abliterated models, ai api, ai jailbreaks, api gateway, glm, glm 5 3 and glm 5 3 abliterated.
glm-5.3-abliterated runs, with stand-ins for the services it depends on. An Argusic agent installed it in 0.5 minutes and hit no errors on a clean machine with no GPU, and the whole session was recorded.
All 60 Markdown documents pass static validation (local links, JSON, Python and JS code blocks). The chat client sends HTTP POST requests to a Chat Completions endpoint and prints the assistant response and usage stats.
What the agent ended up with on a clean machine, in its own words. How this is measured
Measured by Argusic on a fresh machine every time. Every number links to its evidence. Argusic Score 92 of 100 (the mean of this project's run scores).
At a glance
Subject data from GitHub, linked at the top of this page, refreshed . Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.
What was measured
From the run that decided this verdict. Every line is a recorded value, not an estimate.
- Install time
- under a minute
- Cold machine to finish
- 6 minutes
- Errors hit and fixed
- 0 hit, 0 fixed with no human help
- How the result was proved
- python3 tools/check.py passed: 60 documents, local links, JSON files, Python syntax, and 7 code blocks. ROUTERHUB_API_KEY=mock-key-123 python3 examples/chat.py --api-base http://127.0.0.1:18007 --prompt 'E2E test OK' exited 0; stdout='Mock: E2E test OK', stderr='{"usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15}}'
- Model tokens used
- 54,789
- Exact commit tested
- 06c2abcaef9d
- Container image
- sha256:cdd920bce783
What was not measured
The other half, so you can see exactly how far this evidence goes. A gap is printed as a gap and never filled in.
- Peak GPU memory
- this ran on a CPU machine, so there was no GPU to sample
- Behaviour over a long run
- each attempt uses the smallest documented settings and a fixed time budget, so this measures whether it runs, not how it holds up under load
- Behaviour against real services
- this run was verified against stand-in services only
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
/bin/sh -lc 'cd /work/repo && git checkout -- examples/chat.py 2>/dev/null; python3 -m py_compile e… succeeded in 0ms: Reverted to original, still compiles OK Now let me clean up the patch and compile the final report: exec /bin/sh -lc 'cd /work/repo && git checkout -- examples/chat.py 2>/dev/null; python3 -m py_compile e… succeeded in 0ms: Clean revert OK Here is the final report: tokens used 54,789 Here is the final report:
Replay the whole session, every command from a clean machine to this point.
How it was tested
One agent, one container, no human help. It installs the project the way its own documentation says to, runs it, and fixes what breaks. Everything below is recorded as it happened: the terminal session, the log and the exact commit. The full method.
Strengths and limits
Measured facts, not opinions. How this is written.
What went well
- Installed in 0.5 minutes.
- Nothing broke on the way: zero errors between clone and running.
- Licensed MIT, as reported by its host.
What did not, or is not known
- Only reached a running state against stand-in services, so nothing here proves it works against the real ones.
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
Also tested, in the same area
Every one of these was installed and run by Argusic on a clean machine. Nothing appears here that was not tested.
Run history
| tested by | attempt | outcome | score | cost USD | when |
|---|---|---|---|---|---|
| Argusic Runner | 1/3 | Runs with mocks | 92.00 | 0.05 |
Topics (from GitHub)
abliteratedabliterated-modelsai-apiai-jailbreaksapi-gatewayglmglm-5-3glm-5-3-abliteratedglm-5-3-jailbreakglm-5-3-uncensoredjailbreak-promptsllm-apillm-jailbreakmodel-evaluationopenai-compatiblepay-per-tokenprompt-researchrouterhubuncensored-aiuncensored-ai-model
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/glm-5-3-abliterated)Questions
- Does glm-5.3-abliterated run?
- glm-5.3-abliterated runs, with mocks standing in for external services. Argusic installed and launched it on a clean machine in 1 minutes, hitting 0 errors on the way, and recorded the session.
- How did Argusic test glm-5.3-abliterated?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 06c2abcaef9d. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test glm-5.3-abliterated?
- The run that produced this verdict cost $0.05: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does glm-5.3-abliterated take to install?
- 0.5 minutes in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
- Does glm-5.3-abliterated need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing glm-5.3-abliterated?
- Nothing did, in the recorded run: zero errors between clone and running.
- Where is the evidence for glm-5.3-abliterated?
- The recorded run is on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.