Model Detail
Spark-X2.5-4B
▲ 132.8%Spark-X2.5-4B is a code generation model with 4B parameters released by XHToken. The model is registered under the text-generation pipeline tag on Hugging Face, distributed under the permissive apache-2.0 license.
Spark-X2.5-4B ships with 4B parameters. Total weight footprint is approximately 4.1 GB, which is the relevant figure when planning local-inference VRAM. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
Downloads of Spark-X2.5-4B have moved +132.8% 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.
Spark-X2.5-4B is best fit for code completion, repository-scale Q&A, and pair-programming integrations. It is a less obvious choice for one-shot generation of security-critical code without review. 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.
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SPARK: Skeleton-Guided Reasoning Synthesis from Large-Scale Scientific Literature
arXiv:2608.30214v1 Announce Type: new Abstract: Scientific reasoning remains challenging for open-source models, largely due to the lack of high-quality scientific reasoning data. Existing datasets are often dominated by factual recall or formulaic problem solving, with limited emphasis on mechanism
Up to 3.2x Faster Inference with LFM2.5-DSpark
The Prover Is the Judge: Verified Security Software from AI Coding Agents in Ada/SPARK
arXiv:2607.14340v1 Announce Type: cross Abstract: AI coding agents produce code faster than humans can review it. In our approach, the prover is the judge of whether the code is correct. Under a verifier-driven loop, AI agents wrote and verified bare-metal security software in Ada/SPARK spanning cla
SPARK: Susceptibility-Guided Profiling and Steering of Latent Reasoning States in Large Language Models
arXiv:2607.10296v1 Announce Type: new Abstract: Reasoning failures in large language models (LLMs) are usually evaluated from final answers, but a wrong answer does not reveal why the model failed. The same incorrect output may reflect missing capability, an unstable reasoning trajectory, or a failu
Meta enters the crowded AI coding battle with Muse Spark 1.1
Meta's pitch to users is Spark's ability to handle large agentic workloads, fix bugs, and help with large code migrations — the kind of automation that enterprises are increasingly turning to AI companies to provide.
