Model Detail
Qwen2.5-Coder-7B-Instruct
—Qwen2.5-Coder-7B-Instruct is a code generation model with 7B 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 26, GPQA 6, IFEval 61, BBH 29, giving a sense of how it handles instruction following, reasoning, and graduate-level QA in absolute terms.
Qwen2.5-Coder-7B-Instruct is priced at $0.01/M input tokens and $0.03/M output tokens. At this input rate the model sits in the commodity tier and is suitable for high-volume workloads where per-call cost dominates the decision.
Qwen2.5-Coder-7B-Instruct ships as a Qwen2ForCausalLM / 💬 chat models (RLHF, DPO, IFT, ...) architecture with 7B parameters. Total weight footprint is approximately 7.6 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-Coder-7B-Instruct is best fit for code completion, repository-scale Q&A, and pair-programming integrations, and high-volume batch jobs where per-call cost dominates the budget. 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.
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
