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DeepSeek LLM: Scaling Open-Source Language Models with Longtermism research paper by DeepSeek, 2024

DeepSeek · Training and scaling · Jan 5, 2024 · 872 citations · 56 upvotes · unverified

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What it shows

DeepSeek LLM, an open-source language model project, develops a large dataset and employs SFT and DPO to achieve performance surpassing LLaMA-2 70B and GPT-3.5 in various benchmarks and open-ended evaluations.

By DeepSeek-AI, Xiao Bi, Deli Chen and 84 more · arXiv 2401.02954 · PDF · Code

UnverifiedHugging Face's summary; not yet checked by hand.

More from DeepSeek

All 22
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Training and scalingJun 17, 2021LoRA: Low-Rank Adaptation of Large Language ModelsLoRA fine-tunes a large model by training small low-rank matrices, cutting trainable parameters by 10,000 times.Microsoft25.6k citations66 upvotesRead →Training and scalingJan 23, 2020Scaling Laws for Neural Language ModelsLanguage model loss falls as a smooth power law as model size, data and compute grow.OpenAI9,592 citations12 upvotesRead →Training and scalingMar 29, 2022Training Compute-Optimal Large Language ModelsChinchilla: for a fixed compute budget, train a smaller model on more data; parameters and tokens should grow together.Google DeepMind4,035 citations13 upvotesRead →Training and scalingSep 17, 2019Megatron-LM: Training Multi-Billion Parameter Language Models Using Model ParallelismMegatron-LM splits each Transformer layer across GPUs to train models with billions of parameters.NVIDIA3,387 citations5 upvotesRead →Training and scalingMay 15, 2024LoRA Learns Less and Forgets LessLoRA learns less than full fine-tuning on code and math, but forgets less of what the model already knew.Databricks486 citations91 upvotesRead →Training and scalingUnverifiedSep 16, 2024jina-embeddings-v3: Multilingual Embeddings With Task LoRAjina-embeddings-v3, a large-scale text embedding model, achieves state-of-the-art performance in multilingual and long-context retrieval tasks using Low-Rank Adaptation and Matryoshka Representation Learning.Jina AI221 citations35 upvotesRead →
About this paper
Venue
arXiv.org
Citations
872, 79 influential · Semantic Scholar
Upvotes
56 · Hugging Face
Lab
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Changes

CitationsOct 12, 2026Influential citations fell from 80 to 79.
CitationsOct 12, 2026Citations rose from 861 to 872.
CitationsOct 5, 2026Citations rose from 855 to 861.
CitationsSep 25, 2026A first count of 80 influential citations.
CitationsSep 25, 2026A first count of 855 citations.
New paperSep 25, 2026Found by the weekly Hugging Face scan; not yet checked by hand.