What are the hardware requirements for AceCoder Qwen2.5 Coder 7B Ins Rule?

AceCoder Qwen2.5 Coder 7B Ins Rule requires approximately 59 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed AceCoder Qwen2.5 Coder 7B Ins Rule and how large is it?

AceCoder Qwen2.5 Coder 7B Ins Rule is developed by TIGER-Lab, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate AceCoder Qwen2.5 Coder 7B Ins Rule?

AceCoder Qwen2.5 Coder 7B Ins Rule is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

AceCoder Qwen2.5 Coder 7B Ins Rule by TIGER-Lab — VRAM 59GB, 32K context

Name: AceCoder Qwen2.5 Coder 7B Ins Rule
Author: TIGER-Lab

AceCoder Qwen2.5 Coder 7B Ins Rule is an open-source language model by TIGER-Lab. Features: 7b LLM, VRAM: 59GB, Context: 32K, License: mit, Instruction-Based, Code Generating, LLM Explorer Score: 0.24.

Arxiv:2502.01718 Acecoder Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-coder-... Code Codegen Dataset:tiger-lab/acecode-87k En Instruct Qwen Qwen2 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/TIGER-Lab/AceCoder-Qwen2.5-Coder-7B-Ins-Rule

AceCoder Qwen2.5 Coder 7B Ins Rule Parameters and Internals

LLM Name	AceCoder Qwen2.5 Coder 7B Ins Rule
Repository 🤗	https://huggingface.co/TIGER-Lab/AceCoder-Qwen2.5-Coder-7B-Ins-Rule
Base Model(s)	Qwen/Qwen2.5-Coder-7B-Instruct Qwen/Qwen2.5-Coder-7B-Instruct
Model Size	7b
Required VRAM	59 GB
Updated	2026-08-02
Maintainer	TIGER-Lab
Model Type	qwen2
Instruction-Based	Yes
Model Files	9.8 GB: 1-of-3 9.8 GB: 1-of-4 9.9 GB: 2-of-3 9.9 GB: 2-of-4 8.7 GB: 3-of-3 8.7 GB: 3-of-4 2.2 GB: 4-of-4
Supported Languages	en
Generates Code	Yes
Model Architecture	Qwen2ForCausalLM
License	mit
Context Length	32768
Model Max Length	32768
Transformers Version	4.46.3
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	152064
Torch Data Type	float32
Errors	replace

Rank the AceCoder Qwen2.5 Coder 7B Ins Rule capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to AceCoder Qwen2.5 Coder 7B Ins Rule

Best Alternatives	Context / RAM	Downloads	Likes
Qwen2.5 7B Rebase	986K / 15.2 GB	73	2
Qwen2.5 7B Rebase	986K / 15.2 GB	16	2
StockQwen 2.5 7B	128K / 15.2 GB	69	6
DareQwen 2.5 7B	128K / 15.2 GB	11	1
Tessa T1 7B	117K / 15.2 GB	14	10
UIGEN T1.5 7B	117K / 15.2 GB	12	6
Qwen2.5 Coder 7B Instruct	32K / 15.2 GB	2019400	765
Vibe Coder 7B Max	32K / 15.3 GB	460	1
Finsec Detector	32K / 15.2 GB	390	1
Qwen2.5 Coder Malay 7B	32K / 15.2 GB	195	1

Note: green Score (e.g. "73.2") means that the model is better than TIGER-Lab/AceCoder-Qwen2.5-Coder-7B-Ins-Rule.