Model Detail
Qwen2.5-1.5B-Instruct
—Qwen2.5-1.5B-Instruct is a large language model with 1.5B parameters released by Qwen. The model is registered under the text-generation pipeline tag on Hugging Face, distributed under the permissive apache-2.0 license.
Open-LLM-Leaderboard scoring places it at MMLU-Pro 20, GPQA 1, IFEval 45, BBH 20, giving a sense of how it handles instruction following, reasoning, and graduate-level QA in absolute terms.
Qwen2.5-1.5B-Instruct ships as a Qwen2ForCausalLM / 💬 chat models (RLHF, DPO, IFT, ...) architecture with 1.5B parameters. Total weight footprint is approximately 1.5 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.
Qwen2.5-1.5B-Instruct 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.
Probing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders
arXiv:2607.21774v1 Announce Type: new Abstract: Large language models may infer demographic attributes from subtle linguistic cues even when those attributes are not explicitly stated. This pilot study examines whether Qwen2.5-7B-Instruct internally represents Colombian identity, socioeconomic statu
System Report for CCL25-Eval Task 5: New Dataset and LoRA-Fine-Tuned Qwen2.5
arXiv:2606.12392v1 Announce Type: cross Abstract: Recently, large language models (LLMs) have achieved promising progress in the fields of classical Chinese translation and the generation of classical poetry. However, domain-specific research on precise translation and affective-semantic understandi
Tuning Qwen2.5-VL to Improve Its Web Interaction Skills
arXiv:2604.09571v1 Announce Type: cross Abstract: Recent advances in vision-language models (VLMs) have sparked growing interest in using them to automate web tasks, yet their feasibility as independent agents that reason and act purely from visual input remains underexplored. We investigate this se
FinCacheServe: Dependency-Consistent Answer Reuse for Cost-Efficient RAG Serving over Mutable Enterprise Documents
arXiv:2607.26076v1 Announce Type: cross Abstract: Retrieval-augmented generation services over mutable enterprise documents repeatedly execute semantically equivalent analysis requests. Answer reuse can remove GPU-bound generation work, yet response caches require dependency consistency when filings
