AikidoSec/altar-1
text-generation model by AikidoSec
Older snapshot: check the original source for current information.
From the publisher
Model card & documentation
Source previewRead the publisher’s intended use, setup instructions, evaluations and limitations. The original model card is the source of truth.
GLM-5.3 with 34% of its experts removed, at INT4 — 328 GB, built to serve on 4× H200 (Hopper) in vLLM. Altar-1 was calibrated on cybersecurity traces, coding, tool calling, reasoning, and English. Additionally we used multi-lingual wikipedia articles. GLM-5.3 is a 753B mixture-of-experts model: each token uses 8 of 256 expert sub-networks per layer (40B active). REAP (Router-weighted Expert Activation Pruning) scores each expert’s real contribution and deletes the least useful ones — no retraining. This cut keeps 168 of 256 experts per layer. The experts are then INT4 W4A16 (compressed-tensors, AWQ), taken from the cyankiwi/GLM-5.3-AWQ-INT4 base. Only the routed experts are 4-bit; attention, the shared expert, the dense layers, and the head stay BF16. vLLM auto-selects the Marlin MoE kernel. Routing is untouched: 8 experts per token out of the 168 that remain, 40B active parameters, same as the unpruned model. KL divergence vs full BF16: 0.506 nats…
Read the full model card ↗ · Preview checked 2026-11-03T22:46:20.773Z
Inside the original model card — Document outline
- Altar-1 — a 504B parameter Prune of GLM-5.3
- What this is
- How close to the original is it?
- Why these experts
- Serving (vLLM, 4× H200)
- Credits
- Deploying Altar
- License
Headings are captured from the source. Links open the publisher’s document, not a locally hosted copy.
Links from the model card
References supplied by the publisher, not independently verified endorsements. Check the destination before downloading files or entering credentials.
Documentation belongs to its respective authors. Reported project/model license: other. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
Model overview
AikidoSec/altar-1 is a text-generation model repository published by AikidoSec on Hugging Face. A library was not reported.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- text-generation
- Library
- Not reported
- Recent downloads (30 days)
- 1,764
- Cumulative likes
- 195
- Hugging Face trending score
- 100
- Reported safetensors parameters
- 500,825,296,352
- Architecture
- GlmMoeDsaForCausalLM
- License
- other
- Access
- Not gated by Hugging Face
- Created
- 2026-09-07T18:50:43.000Z
- Last modified
- 2026-09-20T12:21:04.000Z
Compatibility and lineage
Language coverage was not reported.
Reported base models: cyankiwi/GLM-5.3-AWQ-INT4
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
Check license terms and access requirements first. Review the model card’s documented loading instructions, evaluations and safety limitations. Benchmark scores are not imported by this directory, and popularity is not an accuracy ranking.
No model weights are downloaded or executed by AltAPIs. Never enable remote model code without reviewing it.
Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-09-29T06:38:06.650Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.