Model Detail
Quasar-Preview
—Quasar-Preview is a code generation model with 18B parameters released by silx-ai. The model is registered under the text-generation pipeline tag on Hugging Face, distributed under the permissive mit license.
Quasar-Preview ships with 18B parameters. Total weight footprint is approximately 16.6 GB, which is the relevant figure when planning local-inference VRAM. The mit license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
Quasar-Preview 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.
QUASAR: Lowering the Loss Floor of Quantization-Aware Training with Loss-Aware Reconstruction
arXiv:2608.13966v2 Announce Type: replace-cross Abstract: As large language model inference shifts toward lower precision, post-training quantization (PTQ) becomes increasingly brittle, making quantization-aware training (QAT) essential for preserving model quality. However, QAT has a structural mis
Quasar: A Programming Language Specialized for LLM Code Actions
arXiv:2506.12202v2 Announce Type: replace-cross Abstract: Large language models (LLMs) often call external tools to solve tasks. One effective strategy is for LLMs to write code, enabling them to use complex control flow such as conditionals and loops. Such code actions are typically represented as
Smooth Quasar-Convex Optimization with Constraints
arXiv:2510.01943v3 Announce Type: replace-cross Abstract: Quasar-convex functions form a broad nonconvex class with applications to linear dynamical systems, generalized linear models, and Riemannian optimization, among others. Current nearly optimal algorithms work only in affine spaces due to the
Minimisation of Quasar-Convex Functions Using Random Zeroth-Order Oracles
arXiv:2505.02281v3 Announce Type: replace-cross Abstract: This paper explores the performance of a random Gaussian smoothing zeroth-order (ZO) scheme for minimising quasar-convex (QC) and strongly quasar-convex (SQC) functions in both unconstrained and constrained settings. For the unconstrained pro
QUASAR: A Universal Autonomous System for Atomistic Simulation and a Benchmark of Its Capabilities
arXiv:2602.00185v2 Announce Type: replace-cross Abstract: The integration of large language models (LLMs) into materials science offers a transformative opportunity to streamline computational workflows, yet current agentic systems remain constrained by rigid, carefully crafted domain-specific tool-
