Model Detail
Qwen2.5-Coder-7B-Instruct-GGUF
—Qwen2.5-Coder-7B-Instruct-GGUF 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 30, GPQA 1, IFEval 34, BBH 28, giving a sense of how it handles instruction following, reasoning, and graduate-level QA in absolute terms.
Qwen2.5-Coder-7B-Instruct-GGUF is priced at $0.06/M input tokens and $0.12/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.
Qwen2.5-Coder-7B-Instruct-GGUF ships as a Qwen2ForCausalLM / 🟢 pretrained architecture with 7B parameters, distributed as a quantized weight variant for lower-VRAM inference. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
Downloads of Qwen2.5-Coder-7B-Instruct-GGUF have moved +0.1% over the trailing seven days. The trend is mildly positive, consistent with a model that is being picked up incrementally rather than going viral. 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.
Qwen2.5-Coder-7B-Instruct-GGUF 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.
Are Verifier Errors Independent Within a GRPO Group? Evidence from Qwen2.5 Rollouts
arXiv:2609.06386v1 Announce Type: new Abstract: Group-based reinforcement learning with verifiable rewards (RLVR) scoresmultiple completions per prompt using automatic verifiers. Analysesbased on independent verifier errors may overlook dependence associatedwith shared answer formats. We investigate
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
