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.

Checking your browser before accessing www.kaggle.com · Click here if you are not automatically redirected after 5 seconds

We cannot provide a description for this page right now

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 ...

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.

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 😢.

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.

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

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.

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.

Comprehensive analysis of DeepSeek-R1 model series, spanning from 1.5B to 671B parameters. Explore version-specific features, use cases, and selection guidelines to help users optimize performance-cost balance. Includes detailed comparison between distilled and full versions for enterprise ...

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.

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

/homer-1943/train-deepseek-r1 · README · 0 Stars · 1 Watching · 0 Forks · Save · Cancel · No release · All · Jupyter Notebook 100.0% Load More · can not load any more · Edit · About · Homepage · Cancel Save · 马建仓 AI 助手 · 尝试更多 ·

September 9, 2025 - With mean input and output sequence lengths of 800 and 3,880 tokens, respectively, the DS-R1 dataset highlights the model’s ability in parsing proficiency, contextual linking, and synthesizing insights from complex and lengthy inputs. The performance metrics chosen for the DeepSeek-R1 benchmark ...

June 23, 2025 - This comprehensive guide demonstrates how to fine-tune DeepSeek-R1 distilled models with custom datasets using memory-efficient techniques like LoRA and Unsloth.