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DataBubble·

Model Detail

Qwen logo

Qwen2.5-7B-Instruct

▼ 0.2%
Provider: QwenCategory: llmPipeline: text-generationParameters: 7B
DB Score
23.8
Downloads
12.6M
Likes
1K
Day
-0.2%
Week
-6.8%
Month
-8.7%
Overview

Qwen2.5-7B-Instruct is a large language model with 7B parameters released by Qwen. The model is registered under the text-generation pipeline tag on Hugging Face, and supports text->text inputs, distributed under the permissive apache-2.0 license.

Performance

Open-LLM-Leaderboard scoring places it at MMLU-Pro 36, GPQA 10, IFEval 77, BBH 34, giving a sense of how it handles instruction following, reasoning, and graduate-level QA in absolute terms.

How we score this →
Pricing & Throughput

Qwen2.5-7B-Instruct is priced at $0.04/M input tokens and $0.1/M output tokens. Operationally the model offers a 33K-token context window, which matters when sizing it for prompt-heavy or latency-sensitive workloads. 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.

Technical

Qwen2.5-7B-Instruct ships as a Qwen2ForCausalLM / 💬 chat models (RLHF, DPO, IFT, ...) architecture with 7B parameters. The published knowledge cutoff is 2024-06-30, so newer events will not be reflected in zero-shot answers without retrieval. 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.

Trending Signal

Downloads of Qwen2.5-7B-Instruct have moved -0.2% over the past 24 hours, -6.8% over the trailing seven days, -8.7% over the trailing thirty days. 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.

Read about databubble_score →
Use Cases

Qwen2.5-7B-Instruct is best fit for general-purpose chat and instruction-following workloads, and high-volume batch jobs where per-call cost dominates the budget. 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.

Download History
Pricing
Input ($/M tokens)
$0.04
Output ($/M tokens)
$0.1
Context Window
33K
Research Paper
arXiv: 2309.00071→
Benchmark Scores
IFEval
76.8
BBH
33.9
GPQA
9.8
MMLU-Pro
36.2
MATH
34.4
MUSR
10.6
Average
33.6
Model Info
Licenseapache-2.0
ArchitectureQwen2ForCausalLM
Type💬 chat models (RLHF, DPO, IFT, ...)
Modalitytext->text
Knowledge Cutoff2024-06-30
Citations2,088 (257 influential)
Recent newsView all news →
Related News
arxivneutral52d ago

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

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