uuu_fine_tune_gpt2

Model · Text generation

Myosotis-1-base

FWKV Project

Myosotis-1-base is the first flagship release from us, introducing a 100-million parameter recurrent language model built on the FWKV architecture. Myosotis-1 is engineered to never truly forget—using a mathematically clamped exponential decay that guarantees an infinite effective context window while maintaining blazing-fast inference on consumer hardware. Instead of pairwise attention, Myosotis uses a fixed-size state vector updated via a gated linear recurrence: $$ St = S{t-1} \odot W + kt \odot vt $$ - \\( W = \text{clamp}(\sigma(w), 0.1) \\) is a learned, constant-per-channel decay. - By clamping the minimum decay to 0.1, the model guarantees that past information decays exponentially…

Model · Text generation

macbert4csc-base-chinese

Ming Xu (徐明)

macbert4csc-base-chinese evaluate SIGHAN2015 test data： 由于训练使用的数据使用了SIGHAN2015的训练集（复现paper），在SIGHAN2015的测试集上达到SOTA水平。 模型结构，魔改于softmaskedbert： 本项目开源在中文文本纠错项目：pycorrector，可支持macbert4csc模型，通过如下命令调用： 当然，你也可使用transformers调用： SIGHAN+Wang271K中文纠错数据集，数据格式： 如果需要训练macbert4csc，请参考https://github.com/shibing624/pycorrector/tree/master/pycorrector/macbert MacBERT is an improved BERT with novel MLM as correction pre-training task, which mitigates the discrepancy of pre-training and fine-tuning. Here is an example of our pre-training task. Except for the new pre-training task, we also incorporate the following techniques. Note that our MacBERT can be directly replaced with the original BERT as there is no…

Model · Text generation

SharperSwarm

Convergent Intelligence

SAGI (Swarm AGI) 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. V3.2 introduces a revolutionary Self-Assessment Layer, allowing the system to predict its own performance, identify skill gaps, and autonomously design its own learning curriculum. 1. Pre-Assessment: Predict success, identify risks, recommend strategy. 2. Execution: Generate with selected strategy. 3. Real-Time Monitoring: Catch and correct errors during generation. 4. Post-Assessment: Update skill…

A 75M parameter decoder-only language model built entirely from scratch in PyTorch — no HuggingFace model classes, no nanoGPT wrapping. Every component (tokenizer, architecture, data pipeline, training loop, SFT) was written from scratch with Claude (Anthropic's AI assistant). Pretraining - ~17.8B tokens of English web text - AdamW (β₁=0.9, β₂=0.95), weight decay 0.1 SFT Fine-tuning - 100K examples from OpenHermes-2.5 - ChatML format with loss masking on user/system tokens Evaluated with log-likelihood scoring (no few-shot): Comparable to GPT-2 (117M) at 0.64× the parameter count. This is a small research model, built to learn how language models work from the ground up. It is not suitable…

Model · Text generation

lightning-105m

AobanZ

Lightning is a small, autoregressive transformer which utilizes FlashAttention and MHA. This model is trained on a variety of books from a dataset(300 MB). This is the expanded version of Lightning-60m. Lightning utilizes FlashAttention and AdamW for performance and capability. Lightning is designed to provide quick, coherent outputs, improved with a larger size and weight. Lightning is intended to be used for research, analysis and fine-tuning, stories and other. It is not intended to be used for professional advice, real writing or any kind of heavy work as generated outputs may be incorrect. Lightning can be used directly for text generation, experimentation, and conversational…

A 97.6M-parameter language model trained from scratch, then fine-tuned for short, polite, everyday English conversation. It is a small, open research and learning model: you can read every line of its training code, run it on a laptop CPU, and see exactly where a model of this size is good and where it fails. - HellaSwag 33.6% (accnorm). That is above GPT-2 small (~30%) and below SmolLM2-135M (43.1%), which saw about 250x more training text. - The custom PyTorch code is included. The model does not use transformers; see How to use. This is QuickTalk run 8. "LMLM97M1" is its published name. 1. Learning and teaching how LLMs work. It is a complete, small, from-scratch GPT, with its training…