Simon Willison achieved impressive code generation results using DeepSeek's new R1 model, running locally on consumer hardware via llama.cpp. He found R1, despite being smaller than other leading models, generated significantly better Python and JavaScript code, producing functional outputs on the first try more consistently. While still exhibiting some hallucination tendencies, particularly with external dependencies, R1 showed a promising ability to reason about code context and follow complex instructions. This performance, combined with its efficient local execution, positions R1 as a potentially game-changing tool for developer workflows.
Simon Willison's blog post, "Promising results from DeepSeek R1 for code," details his initial experimentation with DeepSeek Coder R1, a new closed-source large language model (LLM) specifically designed for code generation. He expresses significant enthusiasm for its performance, particularly compared to other readily available code-generation LLMs like those accessible through the llama.cpp library.
Willison's primary test involves using the models to generate Python code for solving the "n-queens problem," a classic combinatorial challenge. While other models, including those based on the Llama 2 architecture, struggled to produce functioning solutions, DeepSeek Coder R1 consistently generated correct and efficient code. He highlights the model's ability not only to provide a working solution but also to incorporate elegant optimizations, demonstrating a more sophisticated understanding of the problem than exhibited by competing LLMs.
Furthermore, Willison underscores the speed and efficiency of DeepSeek Coder R1. He emphasizes that it generated the correct n-queens solution in a single attempt, contrasting this with the multiple iterations and prompt engineering often required with other LLMs. This speed, combined with the quality of the generated code, significantly enhances the developer workflow.
The post also acknowledges the closed-source nature of DeepSeek Coder R1 and the current lack of public access. Willison obtained access through a private preview and expresses hope for broader availability in the future, given the model's promising performance. He speculates on the potential implications of such a powerful code generation tool becoming widely accessible, suggesting it could significantly impact developer productivity and software development practices. Finally, he briefly touches on the possibility of running DeepSeek Coder R1 using quantized weights via llama.cpp in the future, which could further improve its accessibility and efficiency on consumer hardware.
Summary of Comments ( 525 )
https://news.ycombinator.com/item?id=42852866
Hacker News users discuss the potential of the DeepSeek R1 chip, particularly its performance running Llama.cpp. Several commenters express excitement about the accessibility and affordability it offers for local LLM experimentation. Some raise questions about the chip's power consumption and whether its advertised performance holds up in real-world scenarios. Others note the rapid pace of hardware development in this space and anticipate even more powerful and efficient options soon. A few commenters share their experiences with similar hardware setups, highlighting the practical challenges and limitations, such as memory bandwidth constraints. There's also discussion about the broader implications of affordable, powerful local LLMs, including potential privacy and security benefits.
The Hacker News post "Promising results from DeepSeek R1 for code" (linking to Simon Willison's blog post about LlamaCpp performance) has several comments discussing the implications of efficient local large language models (LLMs).
Several commenters express excitement about the potential of running powerful LLMs on consumer hardware. One user highlights the rapid pace of development, noting that just a few months prior, such performance would have been unimaginable. They anticipate even greater improvements in the near future, speculating about optimized implementations for Apple Silicon and other architectures.
There's a discussion around the potential use cases unlocked by this increased efficiency. Some users mention the possibility of personalized, offline AI assistants, while others envision applications in robotics and embedded systems. One commenter specifically mentions the benefits for developers, allowing them to integrate powerful language models into their workflows without relying on cloud services. This resonates with another comment highlighting the importance of data privacy and the advantages of keeping sensitive information local.
A few comments delve into the technical aspects, discussing the quantization techniques used to reduce the model's size and memory footprint. They also touch on the potential trade-offs between performance and accuracy. One user raises the question of whether these smaller models can truly match the capabilities of their larger counterparts, while another points out that the smaller context window might be a limiting factor for certain tasks.
The conversation also touches upon the broader implications of democratizing access to powerful AI. One commenter expresses concern about the potential misuse of these models, while others celebrate the increased accessibility and the potential for innovation it unlocks.
Finally, some users share their own experiences experimenting with LlamaCpp and other local LLM implementations, providing practical insights and tips for others interested in exploring this technology. They discuss the challenges of setting up and configuring these models, and share their observations on performance and resource usage.