TinyLlama
The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs. We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
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Ling 3.0 Tiny
Ling 3.0 Tiny is an open-weights reasoning model with 7.9B total parameters, 1.3B active parameters, and a 262K-token context window. Built with a mixture-of-experts architecture, it extends the open-weights Pareto frontier for intelligence versus active parameters and is small enough to run locally in many settings. The model scores 25 on the Artificial Analysis Intelligence Index, comparable to gpt-oss-120b (high, 24) while using 15x fewer total parameters and 4x fewer active parameters. This parameter efficiency comes with relatively high token usage, with 213M output tokens required to run the Intelligence Index. Ling 3.0 Tiny also shows substantial improvements in hallucination behavior over Ling-mini-2.0, improving its AA-Omniscience score by 59 points while maintaining similar accuracy. Rather than guessing when uncertain, it attempted only 37% of questions in the evaluation, resulting in a 30% hallucination rate compared with 96% for the previous generation.
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Inkling
Inkling is an open-weights multimodal AI model from Thinking Machines designed as a customizable foundation model for developers, researchers, and enterprises. The model is a Mixture-of-Experts transformer with 975 billion total parameters, 41 billion active parameters, and support for context windows up to 1 million tokens. Inkling was trained from scratch on text, images, audio, and video, giving it native capabilities across reasoning, coding, agentic tool use, vision, audio, factuality, and instruction following. It is built with controllable thinking effort so users can balance performance, latency, and token efficiency for different workloads. The model is available for fine-tuning on Tinker, with playground access, API availability through ecosystem partners, and full weights published on Hugging Face. Built for customization, Inkling gives teams an open-weights base model for building domain-specific AI systems, multimodal agents, coding workflows, research tools, and more.
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Muse Glimmer
Muse Glimmer is a 30-billion-parameter open-weights model from Meta Superintelligence Labs, optimized for always-on local agent workflows. Small enough to run on a Mac or PC with a single consumer GPU, it is designed for local agents, function calling, coding, and LLM-as-a-judge evaluation without depending on cloud infrastructure or network access. The model combines long-horizon execution, precise tool calling, multimodal understanding, long-context memory, and instruction following. It can complete end-to-end agentic tasks, sustain multi-step reasoning across extended workflows, recover from failed or unexpected tool calls, and accept interleaved text and images through a dedicated perception encoder for interpreting screenshots, charts, and documents. Muse Glimmer works with OpenClaw and other agentic orchestration patterns, supports controllable reasoning effort, and is trained on data from more than 100 languages.
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