Small open models for coding, vision and OCR
Ten new open models, from a 1 GB text model to a 35B research model, plus new tools for vision and long documents.
In this issue
- Liquid AI’s LFM3 text model fits in about 1 GB
- Clover writes text in blocks
- SenseNova-Loop runs part of its network twice
- Mid-size models for coding and research
- Two Spark-X models for on-device use
- Vision and long documents
- The short version
Liquid AI’s LFM3 text model fits in about 1 GB
Liquid AI released LFM3, a family of small open-weight models for text, images, audio and decision tasks.
The text model fits in about 1 GB at 4-bit precision. That means each weight is stored in 4 bits, which shrinks the file at some cost in accuracy. Liquid AI built the family for local customization and on-device use.
A model that small runs on ordinary consumer hardware, with no server and no API bill.
Clover writes text in blocks
DiffuSpace released Clover-35B-A3B-Preview, an open model that generates text in blocks instead of one token at a time. It targets fast coding and tool use.
The team reports 515 tokens per second on a single H200 GPU in agent-task tests. That figure comes from DiffuSpace’s own tests.
Most language models write one token, then the next, then the next. Writing a block at once is a way around that limit, and speed is what agents need most when they call tools in long loops.
SenseNova-Loop runs part of its network twice
SenseNova-Loop-9B-Preview is a compact model that reads text and images. It is aimed at document, coding and tool tasks.
Its main idea is reuse. The model runs part of its own network a second time, which adds depth without storing a second copy of those layers.
The extra depth costs compute time, not extra weights to store.
Mid-size models for coding and research
- Nanbeige4.3-33B. A 33B model for coding and professional assistant work. The team plans to use it as a teacher for smaller models.
- Apex-35B-V1.0. Apex Intelligence’s model for coding, agent tasks and scientific research. AI helped develop it by designing experiments and analyzing training results. The release shows research workflows in robotics and fluid simulation.
Two Spark-X models for on-device use
- Spark-X2.6-1.7B. A small on-device model with better coding, planning and tool use. It has three thinking levels that trade speed for reasoning.
- Spark-X2.6-4B. A compact on-device open model. It does better at coding and tool use than the previous version and also has adjustable thinking levels.
Vision and long documents
- GrootTeam models. A collection of models that take images and text as input. They come in several sizes and in compressed formats such as GGUF and FP8, which make them smaller to download and run.
- Tencent WeVisOne-VL. Finds and counts objects, reads text and outlines objects down to the pixel. The 2B and 4B models are downloadable, and Tencent says they replace several separate vision tools.
- Baidu Unlimited OCR 2. Pulls information out of very long documents. It processes pages together with a compact working memory, so memory use grows slowly with each extra page.
The short version
- Several releases target local and on-device use. LFM3, both Spark-X models and WeVisOne-VL are all small enough for that.
- Clover and SenseNova-Loop change how the model runs. Clover writes in blocks, and SenseNova-Loop reuses its own layers.
- The two larger models lean on AI for training. Nanbeige will teach smaller models, and AI helped design the experiments behind Apex.