Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.
Official4
liquid.ai2026-08-12officialabout the LFM2.5 familyHN 11 pts · 3 comments2,057 words
Liquid AI releases LFM2.5-VL-3B, a non-reasoning vision-language model that integrates a SigLIP2 400M NaFlex encoder and doubles its vocabulary to 128K. It pre-trains on ~34T tokens and decodes 228 tokens/s on an Apple M5 Max. The model achieves about 11K tokens per second output throughput on a single NVIDIA H100.written, reviewed, cited
- The model doubles its vocabulary to 128K by extending the existing tokenizer in place to support non-Latin scripts.
- LFM2.5-VL-3B runs on a phone, decoding 20 tokens/s on a Galaxy S26 Ultra for private on-device execution.
- Evaluation uses vLLM 0.26.0 and non-reasoning mode where models are prompted to directly answer without reasoning.
liquid.ai2026-08-21officialabout the LFM2.5 familyHN 18 pts · 0 comments1,933 words
Liquid AI releases DSpark draft models for LFM2.5-1.2B-Instruct, LFM2.5-2.6B, and LFM2.5-8B-A1B, integrating speculative decoding via llama.cpp and SGLang. The draft models use 5 layers with a block size of 9 and total approximately ~300M parameters, achieving up to 3.18x GPU speedup and up to 2.87x on-device gains for the 2.6B variant. While the 1.2B and 2.6B models see consistent improvements, the 8B-A1B variant only achieves an 18% improvement on edge devices due to current MoE implementation constraints in llama.cpp's Metal backend.written, reviewed, cited
- The 8B-A1B draft model adds 65.5M parameters for its Markov head compared to 33.6M in the 1.2B and 2.6B variants.
- For the 1.2B-Instruct draft, validation loss consistently tracks acceptance rate improvements across all training epochs.
- The 8B-A1B draft model achieves only an 18% improvement on edge devices because verifying k tokens activates more experts and weight traffic.
huggingface.co2026-08-06authorityabout the LFM2.5 familyHN 1 pts · 0 comments1,103 words
Liquid AI releases LFM2.5-Encoder-230M and LFM2.5-Encoder-350M, two new models designed for fast long-context inference on CPU. These encoders support an 8,192-token context and are about 3.7× faster than ModernBERT-base at long context lengths. The models are trained with a masked language objective that masks 30% of tokens during training to achieve strong performance on GLUE, SuperGLUE, and multilingual tasks.written, reviewed, cited
- The 350M variant is the choice when accuracy matters most.
- The 230M variant is the choice for tighter hardware or higher throughput.
- A base encoder provides general-purpose representations rather than direct task outputs.
liquid.ai2026-04-27officialabout the LFM2.5 familyHN 2 pts · 0 comments956 words
Analysis1
2tinteractive.com2026-09-24about the LFM2.5 familyHN 1 pts · 1 comments888 words
LiquidAI released LFM2.5-VL-DSpark on September 24, 2026, which accelerates vision-language model training from 20 days to 3 days. The architecture enhances computational efficiency by 20 percent and includes new training data sets curated by experts. It features integration APIs and GDPR compliance details available on Hugging Face.written, reviewed, cited
- The model includes a new neural architecture designed to minimize latency for real-time video analysis.
- The architecture is more energy-efficient, reducing the computational resources and costs associated with training.
