bloomz-560m

Model · Text generation

LibraMindMini

Alby13

LibraMind is a 565M-parameter chat model pretrained from scratch by alby13 on a single consumer GPU (NVIDIA RTX 4090). It is a hybrid architecture that interleaves 18 Gated DeltaNet linear-recurrent layers with 6 Gated Attention layers (a repeating DDDA × 6 pattern), with Block Attention Residuals and logit soft-capping, trained with the Muon optimizer. It went through a full five-stage pipeline: pretraining, midtraining, supervised fine-tuning, preference tuning, and RL with verifiable rewards. - Research on small hybrid recurrent/attention language models - Studying low-resource, single-GPU training pipelines (pretraining through RL) and the Muon optimizer at small scale - Experiments…

Model · Text generation

Qwen3-0.6B-Base

Qwen

Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Building upon extensive advancements in training data, model architecture, and optimization techniques, Qwen3 delivers the following key improvements over the previously released Qwen2.5: Qwen3-0.6B-Base has the following features: For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and Documentation. The code of Qwen3 has been in the latest Hugging Face transformers and we advise you to use the latest version of transformers. With transformers<4.51.0, you will…

Model · Text generation

Symbiotic-1B

Convergent Intelligence

Purpose: Lightweight, memory-augmented reasoning model for CPU and embedded inference SymbioticLM-1B is the compact version of the SymbioticAI architecture. It fuses Qwen’s rotary transformer design with a symbolic processing pipeline and a persistent episodic memory. Though smaller in parameter count, it retains the full cognitive engine: symbolic memory, dynamic thought evolution, and entropy-gated control. This model is ideal for symbolic reasoning in constrained environments — like research agents, lightweight assistants, and memory-efficient logical processing. - Procedural planning, math modeling, small-code generation - Less fluent in free-form language than larger variants…

This model is a fine-tuned version of Qwen/Qwen3-0.6B on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 4 - evalbatchsize: 8 - gradientaccumulationsteps: 16 - totaltrainbatchsize: 64 - lrschedulertype: cosine - lrschedulerwarmupsteps: 100 - numepochs: 3 - Transformers 5.17.0 - Pytorch 2.11.0+cu128 - Datasets 5.0.1 - Tokenizers 0.23.2

Converted with mlx-lm 0.31.3. A six-case deterministic sanity check matched three upstream reference strings exactly. The remaining differences included a retained leading “So,” punctuation/ordinal variation, and omission of “tomorrow” in one correction case. This is a small functional check, not the upstream 7,519-case evaluation; assess the 4-bit build on your own transcripts. A 0.6B-parameter text normalizer for speech-to-text output. It takes a raw ASR transcript and rewrites it as clean written text: fillers removed, false starts and self-corrections resolved to the value the speaker landed on, punctuation and capitalization applied, and spoken numbers, dates, times, currency and email…

Converted with mlx-lm 0.31.3. A six-case deterministic sanity check matched four upstream reference strings exactly. The two differences were a retained leading “So,” and one comma variation. This is a small functional check, not the upstream 7,519-case evaluation. A 0.6B-parameter text normalizer for speech-to-text output. It takes a raw ASR transcript and rewrites it as clean written text: fillers removed, false starts and self-corrections resolved to the value the speaker landed on, punctuation and capitalization applied, and spoken numbers, dates, times, currency and email addresses rendered in written form. On a held-out set of 7,519 English cases it reaches 94.8% token accuracy, and…