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Liquid AI releases LFM2.5-2.6B, a compact agentic model designed to run entirely on-device, enabling free inference, low latency, and privacy. The post details its training pipeline including SFT, teacher specialization, distillation, and agentic RL.
Liquid AI releases LFM2.5-2.6B, a compact agentic model designed for on-device deployment, supporting tool calling and multi-step workflows with efficient inference on CPUs and GPUs.
SupraLabs releases Supra2-100M Base and Instruct models, a new small language model family with community-driven improvements, benchmarks, and a GGUF version.
Presents B1ade, a minimalist RAG architecture with a 335M zero-training embedding model and a 1B SLM trained via GRPO on 723M tokens, showing emergent attribution behavior and competitive QA performance without large-scale pretraining.
Nanbeige4.2-3B is a compact 3B parameter general agentic model pretrained from scratch with a Looped Transformer, achieving strong agentic and reasoning performance, outperforming larger models on diverse benchmarks. The model and code are open-sourced.
Compares two new AI models for agentic workloads: the compact Nanbeige4.2-3B with a looped transformer architecture and the large Mixture-of-Experts Laguna S2.1, both released on Hugging Face.
OpenLanguageModel (OLM) is an open-source PyTorch library for building and pretraining small language models with readable, composable architecture code, bridging education and research.
OpenBMB released MiniCPM5-2B, a 2 billion parameter language model, currently not yet available on Hugging Face.
This paper investigates whether GRPO post-training improves a small (4B-8B) language and vision-language model web agent. It finds a controlled null result: no configuration yields credible gains on mastered tasks, and moderate-to-high learning rates cause degradation or collapse, revealing a double dissociation between degrade and collapse regimes.
Bilibili releases Index-1.9B, a series of open small language models pre-trained on 2.8 trillion tokens, achieving competitive performance on benchmarks. The four models include base, pure (no instruction data), chat, and a character model with retrieval-augmented generation for role-playing.
A 270M parameter language model trained from scratch on English Wikipedia and instruction-tuned for conversational AI, developed as an independent research project.
Wiola is a novel Small Language Model architecture introducing five independently designed components—SRPE, GCLA, ATM, DSFF, and WiolaRMSNorm—aimed at improving efficiency and coherence, released in sizes from 120M to 1.5B parameters and integrated with HuggingFace Transformers.
Details an approach to train a small LLM judge for evaluating agent outputs, replacing costly frontier models, with a Claude Code plugin for deployment.
Liquid AI releases LFM2.5-230M, a small 230M parameter model optimized for fast inference on CPUs, NPUs, and GPUs, targeting agentic tasks on devices like phones and robots.
Liquid AI released LFM2.5-230M, a compact 230M-parameter hybrid model optimized for on-device deployment with fast edge inference speeds (213 tok/s on Galaxy S25 Ultra) and built for agentic tasks via reinforcement learning.
SupraLabs released supra-title-FFT-preview, a full fine-tuned 0.4B parameter model for chat title generation, trained on 115K samples — nearly 10x larger than their previous dataset.
Nanbeige 4.1, a 3B model, outperforms Qwen3-30b-A3b and Qwen 3.5 4b in coding tasks with focus on algorithmic efficiency, achieving long horizon tasks with 600+ tool calls.
VibeThinker, a 3B parameter model fine-tuned on Qwen 2.5, achieves performance comparable to Claude Opus 4.5 and much larger models like DeepSeek v3 through innovative post-training that includes multi-path thinking and staged training on math, coding, and science.
Microsoft is testing Phi Silica support on Nvidia GPUs, allowing developers to run the small language model locally on Windows devices with RTX 30-series or newer GPUs, though it lacks NPU-only features like prompt compression.
Weibo's VibeThinker-3B, a 3B parameter model, claims to match or exceed the reasoning performance of much larger models like DeepSeek V3.2 and Gemini 3 Pro on math and coding benchmarks, sparking debate over benchmark reliability and the necessity of scaling.