JMicro-10M-chat-research-20261018

Model · Text generation

ldt-10m

Compactbot

A 10,284,480-parameter LLaMA-style text model, trained from scratch. This is a verified first checkpoint for the LDT-10M request (model-requests #12, DedeProGames) — real weights, real training, but undertrained (see the honest status below). It is not a quality release yet; the card states that plainly. Standard LLaMA block, no sliding window, no GQA: The parameter count is the learnable total: the raw safetensors sum is 14,216,640, which double-counts the tied embedding (tok.weight 12288×320 = 3,932,160) that head.weight aliases. Tied, the true count is 10,284,480. - Trained from scratch (no base model). The model learned real context — val loss 4.602 is well below the 7.38 unigram floor…

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Model · Text generation

BAAR6-9M-7

BAAR

BAAR5-9M is an ultra-compact multilingual conversational model designed to enable real-time chat under minimal hardware conditions. With just 9 million (9M) parameters, it brings lightweight, interactive dialogue capabilities across 11 languages to extreme resource-constrained environments, embedded devices, and on-device platforms where standard LLMs cannot operate. BAAR5-9M은 최소한의 하드웨어 여건과 극단적인 리소스 제약 환경에서도 11개 언어로 실시간 대화(Chat)가 가능하도록 설계된 900만(9M) 파라미터 규모의 초경량 다국어 언어 모델입니다. 일반적인 대형 모델 구동이 불가능한 초저사양 엣지 및 임베디드 기기에서도 즉각적인 대화형 추론과 초경량 실시간 RAG 시스템 구축을 지원합니다. - Non-Commercial Use Only (비상업적 연구·개인 이용 한정): This model is strictly provided for non-commercial, research, and educational purposes only.…

A tiny pirate-themed GPT trained from scratch on a piratized version of TinyStories, then SFT-tuned. Built as a learning project — closer to nanoGPT than to a production LM. - model.safetensors — model weights. - config.json — architecture config (load into training.config.Config). - piratebpe.json — tokenizer (load with tokenizers.Tokenizer.fromfile). - trainingmetadata.json — full training config + metrics snapshot. - banner.png — the banner above. This model is not a transformers model — it uses the custom GPT class from this repo. - Trained on a small synthetic corpus (TinyStories, piratized). Vocabulary, grammar, and world knowledge are extremely narrow. - Short context window (256…

Model · Text generation

hypa-tiny-keys

Hypa-Intelligence

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

GPT-Neo storyteller (nominal 1M; actual 3,745,984 parameters including the 50,257-token embedding) trained on TeenyTinyStories (TTS): 2,491,490 stories (498.5M tokens with EOS) generated by roneneldan/TinyStories-Instruct-33M. batch 1280 x 512, constant LR 5e-4, weight decay 0.1, betas (0.9, 0.95), clip 1.0. - TS-valid capped to stories of at most 400 tokens (20,711 stories): 2.369 / ppl 10.69 Research on small-scale synthetic pretraining data. Generates simple children's stories; sample at temperature 0.8-1.0. See the TeenyTinyStories paper for the corpus recipe and judge evaluations. Apache-2.0.