Open Reasoner Zero 7B GGUF
Original model: Open Reasoner Zero 7B
Model creator: Open Reasoner Zero
An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model
🌊 We introduce Open-Reasoner-Zero, the first open source implementation of large-scale reasoning-oriented RL training focusing on scalability, simplicity and accessibility.
To enable broader participation in this pivotal moment we witnessed and accelerate research towards artificial general intelligence (AGI), we release our source code, parameter settings, training data, and model weights. Please refer to our paper for more insights.
This repo contains GGUF format model files for Open Reasoner Zero’s Open Reasoner Zero 7B.
What is GGUF?
GGUF is a file format for representing AI models. It is the third version of the format, introduced by the llama.cpp team on August 21st 2023.
Converted with llama.cpp build 4764 (revision 7ad0779), using autogguf-rs.
Prompt template: Open Reasoner Zero
A conversation between User and Assistant. The User asks a question, and the Assistant solves it. The Assistant first thinks about the reasoning process in the mind and then provides the User with the answer. The reasoning process is enclosed within <think> </think> and answer is enclosed within <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think> <answer> answer here </answer>.
User: You must put your answer inside <answer> </answer> tags, i.e., <answer> answer here </answer>. And your final answer will be extracted automatically by the \\boxed{} tag.\nThis is the problem:
{{prompt}}
Assistant: <think>
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- create & save Characters with custom system prompts & temperature settings
- download and experiment with any GGUF model you can find on HuggingFace!
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- try it out yourself today, on Testflight!
- if you already have the app, download Open Reasoner Zero 7B now!
- cnvrsai:///models/search/hf?id=brittlewis12/Open-Reasoner-Zero-7B-GGUF
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Original Model Evaluation
Figure 1 | Evaluation performance of Open-Reasoner-Zero-{7B, 32B}. We report the average accuracy on the benchmark dataset for each question with 16 responses. Notably, Open-Reasoner-Zero-32B outperforms DeepSeek-R1-Zero-Qwen-32B on the GPQA Diamond benchmark while only requiring 1/30 of the training steps. We are continuing to scale up these RL settings until this preprint is released, as there is no sign of saturation.
