DeepSeek fine-tuning secrets are now open source

The domestic AI community has made a major breakthrough. The Colossal-AI team has open-sourced the DeepSeek V3/R1 full-blooded version of the fine-tuning tool chain, breaking the monopoly of large AI models and achieving technology democratization.
Core content:
1. DeepSeek V3/R1 full-blooded version of the fine-tuning tool chain is open source, turning the AI behemoth into a private model
2. LoRA technology greatly reduces the fine-tuning hardware requirements and enables low-cost private model creation
3. Engineering optimization breakthroughs, greatly reducing training costs, responding to the challenge of chip blockade, and demonstrating China's AI strength
As we all know, DeepSeek-R1 follows the MIT License, allowing users to train other models with R1 through distillation technology. It became popular all over the world after it was launched on January 20. Subsequently, DeepSeek-R1 launched the API, opening the thinking chain output to users, and can be called by setting model='deepseek-reasoner'.
Just now, the domestic AI community welcomed a major event: the Colossal-AI team open-sourced the full-blooded fine-tuning toolchain of DeepSeek V3/R1, taming the "AI behemoth" with 671 billion parameters into a "private model" in the hands of developers. I personally think it is not only a carnival of technological democratization, but also a turning point in the reconstruction of the global AI industry landscape.
From "computing power hegemony" to "everyone can fine-tune"
In the past, the competition for large AI models was essentially a contest between computing power and capital.
Giants such as OpenAI and Google have built a high wall of technological monopoly with sky-high training costs and closed-source ecosystems. The open source of DeepSeek's full-blooded fine-tuning tool directly breaks this narrative logic.
The LoRA (low-rank adaptation) technology provided by Colossal-AI reduces the fine-tuning hardware requirements of DeepSeek V3/R1 by nearly 10 times. For example, the full parameter fine-tuning that originally required thousands of GPUs can now be completed with only 24 H100s or 32 Huawei Ascend 910Bs.
With CPU Offload technology, developers can even achieve lightweight fine-tuning on consumer-grade hardware.
"Dimensionality reduction attack" allows small and medium-sized enterprises to build private models at low cost, completely breaking the monopoly of large models.
The DeepSeek team's breakthrough in engineering optimization is even more disruptive: through the "Multi-Head Latent Attention Mechanism" and "FP8 Mixed Precision Training", its training cost is an order of magnitude lower than OpenAI.
The engineering capability of "building a temple in a snail shell" not only responds to the challenge of the US chip blockade, but also verifies the unique path of the Chinese AI team in the coordinated optimization of algorithms and systems.
The reinforcement learning framework (such as GRPO, DPO, SimPO) and flexible reward function interface provided in the open source tool chain allow developers to quickly build vertical field models. For example, the medical industry can design a dual reward mechanism of "accuracy + compliance", and the financial field can add risk control indicators.
The out-of-the-box design is giving rise to a new ecosystem of “fine-tuning as a service”.
From "Model Involution" to "Scenario is King"
The release of DeepSeek’s fine-tuning cheats marks the official entry of AI competition into the “deep waters of scenarios”.
The winners in the future will no longer be those who compete in terms of parameter scale, but those who accurately capture the pain points in vertical fields.
Taking the medical scenario as an example, a cancer hospital built an "anti-tumor drug adverse reaction assistant" based on DeepSeek. Through fine-tuning, it achieved the integration of professional terminology understanding and evidence-based medicine knowledge base, with an accuracy rate of over 90%.
The "small data + big model" model is being replicated in finance, law, manufacturing and other fields. The multi-round dialogue optimization solutions (such as the masked attention mechanism) provided in the tool chain make it possible to automate complex business processes.
Previously, Chinese companies had to rely on Microsoft Cloud to use the GPT-4 API, but the open source of DeepSeek directly shook this situation.
Alibaba Cloud and Tencent Cloud have launched hosting services based on DeepSeek, which cost only 1/5 of GPT-4. The more far-reaching impact is that the decentralization of model fine-tuning capabilities allows cloud vendors to transform from "computing power dealers" to "solution integrators." For example, after Tencent Yuanbao is connected to DeepSeek, the search traffic is deeply bound to the mini-program ecosystem, forming a closed business loop.
The UBS report pointed out that AI investment is shifting from hardware-driven to software-driven.
DeepSeek's low-cost feature allows software companies to quickly implement applications in a "light asset" model, and its valuation logic shifts from "computing power consumption" to "scenario penetration rate."
A typical example is Kingsoft Office: by fine-tuning the smart assistant developed by DeepSeek, its ARPU (revenue per user) increased by 17% in 3 months, while R&D costs only increased by 5%.
The "undercurrent" behind the carnival
Despite its promising prospects, this technological revolution still has to overcome three obstacles:
1. Breakthrough of “Data Island”
The current separation of private and public data in enterprises makes it easy for fine-tuning models to fall into the trap of "superficial intelligence". For example, although a customer service model of a bank can have smooth conversations, it has become a "high-level repeater" because it cannot access the internal risk control system.
Solving this problem requires a more open federated learning framework, which may lead to a game between data security and trade secrets.
2. The “sustainability” test of the open source community
Although DeepSeek's open source strategy has won good reputation, its commercial monetization path remains unclear.
Referring to the Red Hat model, in the future, it may be necessary to achieve profitability through enterprise-level support services (such as customized fine-tuning and compliance certification). However, how to balance community contributions and commercial interests will be the ultimate test for China's open source ecosystem.
3. The “Gray Area” of Technology Ethics
The prevalence of fine-tuning capabilities also brings the risk of abuse. Recently, there have been cases of using DeepSeek to generate false medical advice, and existing open source agreements have limited constraints on the use of models.
The industry urgently needs to establish an "AI Ethics Addendum" similar to the Apache Protocol to clearly define prohibited usage scenarios.
I think the open source release of the DeepSeek fine-tuning tool is ushering in a new era.
(1) It verifies the possibility of "small team + engineering innovation" to fight against computing power hegemony.
(2) It promotes the migration of AI value from the model layer to the application layer and reconstructs the global division of labor system.
(3) It provides a differentiated path for China to participate in the AGI (artificial general intelligence) competition, not blindly pursuing trillion-dollar parameters, but building technological influence through an open source ecosystem.
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As Microsoft CEO Nadella said: "DeepSeek proves that the future of AI does not belong to a certain giant, but to all developers who dare to innovate."
Only hard work can bring a future. In this technological equality movement, Chinese companies have for the first time taken the position of rule makers. The next script may be jointly written by countless fine-tuned "small models".