SmolLM2-135M

Model · Text generation

SmolLM2-135M-Instruct

Hugging Face Smol Models Research

SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device. More details in our paper https://arxiv.org/abs/2502.02737 SmolLM2 demonstrates significant advances over its predecessor SmolLM1, particularly in instruction following, knowledge, reasoning. The 135M model was trained on 2 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new filtered datasets we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own…

Model · Text generation

gpt2

OpenAI community

Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in and first released at this page. model. Content from this model card has been written by the Hugging Face team to complete the information they provided and give specific examples of bias. GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs…

Model · Text generation

GPT-X2-125M-CIx-Long-Context

Convergent Intelligence

This model is a custom-code derivative of AxiomicLabs/GPT-X2-125M, adapted for experimental long-context causal language modeling and architecture research. The repository includes a Hugging Face Transformers-compatible GPT-X2 implementation with optional Symplectic Metric-RoPE Governor support and training utilities built around CIxOpt, a heterogeneous optimizer developed for efficient parameter routing across large projection matrices, sensitive normalization parameters, and optional governor modules. The model is intended as a research checkpoint for compact long-context generation, positional encoding experiments, optimizer testing, and continued fine-tuning. This implementation uses a…

English | 中文 A 143M-parameter bidirectional Chinese↔English translation model, instruction-tuned on the Haidass1.5-143M base — the strongest zh⇄en translator at this scale among general chat-architecture models. Drafter-143M: a control model with identical configuration, data and training recipe, except that it starts from random initialization instead of the pretrained base — used to quantify the contribution of base-model pretraining. OPUS-MT models are single-directional — one independent 78M model per direction; "-" marks directions a model does not serve. The same models re-evaluated on FLORES+ devtest (released 2026; zero overlap with dev): devtest sentences do not overlap with dev.…

Model · Text generation

Haidass-Translate-143M-Instruction

Ziyang Mei

English | 中文 The instruction-tolerant sibling of DALabCommunity/Haidass-Translate-143M: same 143M zh⇄en translation training, plus 9.1% cleaned general-domain data (STEPFUN ShareGPT) mixed in. Translation scores are within 0.3 BLEU of the pure-translation version, and the model retains limited general instruction-following ability that the pure-translation version does not have. Drafter-143M: a control model with identical configuration, data and training recipe, except that it starts from random initialization instead of the pretrained base — used to quantify the contribution of base-model pretraining. OPUS-MT models are single-directional — one independent 78M model per direction; "-"…

This model is a fine-tuned version of fpadovani/arb-arab-100mb-ppt-shuff-dyck-100mbseed10. It has been trained using TRL. This model was trained with SFT.