Model Detail
flint-nvfp4-gguf
▲ 19.5%flint-nvfp4-gguf is a large language model released by RecursiveSI. The model is registered under the text-generation pipeline tag on Hugging Face, distributed under the permissive apache-2.0 license.
The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
Downloads of flint-nvfp4-gguf have moved +19.5% over the past 24 hours. That is a slight downtrend, consistent with normal cooling as newer models compete for the same workloads. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.
flint-nvfp4-gguf is best fit for general-purpose chat and instruction-following workloads. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Recursive launches Flint, a laptop coding model it says was largely built by AI
Startup Recursive has released Flint, a free, laptop-sized coding model built largely by its own AI research system, as an early test of AI that improves itself.
FLINT: Fixed-budget Late Interaction
arXiv:2610.27273v1 Announce Type: cross Abstract: Multi-vector retrieval models such as ColBERT often outperform single-vector models of comparable scale by matching queries and documents at the token level. However, retaining a vector for every token substantially increases storage requirements and
FLINT: Fast Lightweight Inference for Traversability
arXiv:2609.26857v1 Announce Type: cross Abstract: Navigation in off-road conditions is challenging due to the lack of structure. There is no fixed vocabulary for what is traversable. The traversability depends on both the environment and the embodiment's dynamics. Neither of these two variables can
FLINT: Fingerprinting Federated Learning Architectures from 5G PHY-Layer Side Channels
arXiv:2607.15469v1 Announce Type: cross Abstract: Federated Learning (FL) over 5G cellular networks protects raw data but remains vulnerable to side-channel leakage. Prior fingerprinting attacks assume packet-level network visibility, an assumption that does not hold at the 5G Physical (PHY) layer,
