A 69M parameter causal language model built on the Mixture-of-Attentions (MoA) architecture — distance-based metric attention that respects the triangle inequality by construction, not approximation. Every attention head operates in a proper metric space. The geometry is enforced, not hoped for. Standard transformers compute attention as a dot product: Q·Kᵀ. This has no geometric meaning — it's a bilinear form, not a distance. Two tokens can be "close" by dot product while violating basic metric properties. MoA replaces this with negative squared distance under a learned diagonal Mahalanobis metric, then enforces the triangle inequality through a regularizer over random triples sampled…
Open weights
cc
69M parameters
1,024 tokens
transformers
DistilGPT2 (short for Distilled-GPT2) is an English-language model pre-trained with the supervision of the smallest version of Generative Pre-trained Transformer 2 (GPT-2). Like GPT-2, DistilGPT2 can be used to generate text. Users of this model card should also consider information about the design, training, and limitations of GPT-2. CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes. As the developers of GPT-2 (OpenAI) note in their model card, “language models like GPT-2 reflect the biases inherent to the systems they were trained on.” Significant research has explored bias and…
Open weights
apache-2.0
88M parameters
transformers
SAGI is a novel causal language model that integrates swarm intelligence dynamics with transformer architecture. The model treats cognition as a dynamic, adaptive system where multiple internal "agents" collaborate through differentiable routing, trust mechanisms, and shared memory. - Episodic + Semantic Memory: Dual memory system with trainable retrieval utility The swarm processes observations derived from token embeddings, updating its internal state S. This state conditions the transformer's attention patterns and feed-forward activations via learned projections, creating bidirectional information flow between symbolic (tokens) and subsymbolic (swarm dynamics) processing. - Educational…
Open weights
apache-2.0
53M parameters
2,048 tokens
transformers
The Pythia Scaling Suite is a collection of models developed to facilitate interpretability research (see paper). It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. We also provide 154 intermediate checkpoints per model, hosted on Hugging Face as branches. The Pythia model suite was designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a…
Open weights
apache-2.0
96M parameters
2,048 tokens
transformers
This model is a fine-tuned version of fpadovani/eng-latn-10mb-ppt-Dp-100mb-packedseed3407. It has been trained using TRL. This model was trained with SFT.
Open weights
39M parameters
transformers
This model is a fine-tuned version of fpadovani/eng-latn-10mb-ppt-Dp-10mb-packedseed3407. It has been trained using TRL. This model was trained with SFT.
Open weights
39M parameters
transformers
