dill-lab/GeoLadder-75M-8B-bs64-lr0.0053-wd0.1-decay0.01
AI model by dill-lab
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.
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with Disentangling Geometry, Performance, and Training in Language Models (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · arXiv · OpenReview The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models but not universally, and low rank co-occurs with late-stage degradation rather than causing it. Pre-training choices such as batch size and weight decay strongly shape these geometric metrics, and none of them…
Read the full model card ↗ · Preview checked 2026-10-30T01:15:23.242Z
Inside the original model card — Document outline
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. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
Model overview
dill-lab/GeoLadder-75M-8B-bs64-lr0.0053-wd0.1-decay0.01 is a task-unspecified model repository published by dill-lab 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
- Not reported
- Library
- Not reported
- Recent downloads (30 days)
- 0
- Cumulative likes
- 1
- Hugging Face trending score
- 1
- Reported safetensors parameters
- Not reported
- Architecture
- OLMoForCausalLM
- License
- Not reported — check the model card
- Access
- Not gated by Hugging Face
- Created
- 2026-10-13T06:23:14.000Z
- Last modified
- 2026-10-13T06:23:22.000Z
Compatibility and lineage
Language coverage was not reported.
Base-model lineage was not included.
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 2026-10-13T06:41:58.956Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.