DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks. To support the research community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen.

We are releasing SYNTHETIC-1, the largest open reasoning dataset generated from Deepseek-R1, collaboratively generated by compute contributors across the globe.

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We release Mixture-of-Thoughts--a curated reasoning dataset of 350k verified traces distilled from R1. The dataset spans tasks in mathematics, coding, and science, and is designed to teach language models to reason step-by-step.

dataset is evaluated using problems from 10 Div.2 contests along with expert-crafted test cases, after which the expected ratings and percentages of competitors are calculated. SWE-Bench · verified results are obtained via the agentless framework (Xia et al., 2024). AIDER-related · benchmarks are measured using a "diff" format. DeepSeek-R1 ...

In this blog post, we used the Synthetic Data Generator to create a custom and high-quality synthetic reasoning dataset for solving Python coding problems with DeepSeek-R1-Distill-Qwen-32B. We then fine-tuned a smaller model, DeepSeek-R1-Distill-Qwen-1.5B, using this dataset.

The release of DeepSeek-R1 is an amazing boon for the community, but they didn’t release everything—although the model weights are open, the datasets and code used to train the model are not 😢.

If you want to generate a high-quality dataset, you are advised to refer to the dataset generation process in OpenR1-Math-220k. ... Deploy the DeepSeek-R1 inference service locally by referring to MindSpore-Lab/DeepSeek-R1 | Modelers or use the public API service.

DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks. To support the research community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen.

A step-by-step guide to generating high-quality mathematical reasoning datasets with CAMEL-AI and DeepSeek R1.

Distillation takes the knowledge of a large, powerful “teacher” model (DeepSeek R1) and transfers it to smaller “student” models. Using a large dataset of reasoning examples, the outputs of DeepSeek R1 are used as the target answers.

January 27, 2025 - In this tutorial, we will fine-tune the DeepSeek-R1-Distill-Llama-8B model on the Medical Chain-of-Thought Dataset from Hugging Face. This distilled DeepSeek-R1 model was created by fine-tuning the Llama 3.1 8B model on the data generated with DeepSeek-R1.

The recommended dataset size for fine-tuning DeepSeek’s R1 model typically ranges from **10,000 to 100,000 examples**, d

By the end, you'll be able to fine-tune almost any large language model with a dataset of your choice. Before we begin, we need to install the Unsloth library along with its latest updates from GitHub. %�pture !pip install unsloth !pip install --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git · Now that Unsloth is installed, we can proceed to load our model and tokenizer. Now, we will load the DeepSeek model using Unsloth’s optimized methods. I am using the DeepSeek-R1-Distill-Llama-8B model.

DeepSeek-R1 achieves state-of-the-art results in various benchmarks and offers both its base models and distilled versions for community use.

January 31, 2025 - Once this is done, DeepSeek creates a supervised fine-tuning dataset of around 600K reasoning samples from this last checkpoint and 200K samples that went into the post-training of v3 itself before fine-tuning v3-base on all of this data for two epochs. If the average length per sample is around 8K, which is in line with what R1-Zero achieves by the end of training, this SFT dataset has a total of 800K * 8K = 6.4B tokens and so the fine-tuning itself has a negligible cost.

The exact training dataset size for DeepSeek’s R1 model has not been publicly disclosed by the developers. While specific numbers are unavailable, the scale of datasets for state-of-the-art language models typically ranges from hundreds of billions to trillions of tokens.

dataset version metric mode deepseek-r1-distill-qwen-7b-turbomind ---------------------------------- --------- ------------- ------ --------------------------------------- MATH - - - AIME2024-Aveage8 - naive_average gen 56.25

Learn how to fine-tune DeepSeek R1 language models using custom instruction datasets.

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