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OpenR1 Distill 7B by open-r1

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OpenR1 Distill 7B is an open-source language model by open-r1. Features: 7b LLM, VRAM: 15.2GB, Context: 32K, License: apache-2.0, LLM Explorer Score: 0.2.

  Arxiv:2504.21318 Base model:finetune:open-r1/qw... Base model:open-r1/qwen2.5-mat...   Conversational Dataset:open-r1/mixture-of-tho...   En   Endpoints compatible   Qwen2   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/open-r1/OpenR1-Distill-7B 

OpenR1 Distill 7B Benchmarks

LLME Score: 0.19927
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
OpenR1 Distill 7B (open-r1/OpenR1-Distill-7B)
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OpenR1 Distill 7B Parameters and Internals

LLM NameOpenR1 Distill 7B
Repository 🤗https://huggingface.co/open-r1/OpenR1-Distill-7B 
Base Model(s)  open-r1/Qwen2.5-Math-7B-RoPE-300k   open-r1/Qwen2.5-Math-7B-RoPE-300k
Model Size7b
Required VRAM15.2 GB
Updated2026-05-06
Maintaineropen-r1
Model Typeqwen2
Model Files  4.9 GB: 1-of-4   4.9 GB: 2-of-4   4.3 GB: 3-of-4   1.1 GB: 4-of-4   0.0 GB
Supported Languagesen
Model ArchitectureQwen2ForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.52.0.dev0
Tokenizer ClassQwen2Tokenizer
Padding Token<|endoftext|>
Vocabulary Size152064
Torch Data Typebfloat16
Errorsreplace

Best Alternatives to OpenR1 Distill 7B

Best Alternatives
Context / RAM
Downloads
Likes
Qwen2.5 7B Preview986K / 15.2 GB50
Qwen2.5 7B Instruct 1M986K / 15.4 GB32350369
Hush Qwen2.5 7B V1.2986K / 15.2 GB31
Hush Qwen2.5 7B V1.1986K / 15.2 GB61
Hush Qwen2.5 7B Preview986K / 15.2 GB400
Hush Qwen2.5 7B V1.4986K / 15.2 GB41
Hush Qwen2.5 7B V1.3986K / 15.2 GB42
Hush Qwen2.5 7B RP V1.4 1M986K / 15.2 GB172
Qwen 2.5 7B Exp Sce986K / 15.2 GB232
Qwen2.5 7B MixStock V0.1986K / 15.2 GB433
Note: green Score (e.g. "73.2") means that the model is better than open-r1/OpenR1-Distill-7B.

Rank the OpenR1 Distill 7B Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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  

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