The recent release of Steiner, an open-source attempt to reproduce OpenAI's o1 capabilities, has sparked significant interest in the developer community, particularly regarding its accessibility and deployment options. While the model shows promising results with a +5.56 improvement on the GPQA-Diamond dataset, the community's focus has largely centered on practical implementation aspects and deployment methods.
Easy Access Through Ollama
A major development highlighted in community discussions is the availability of Steiner through Ollama, making it more accessible to everyday users. Users can now run the model using a simple command:
ollama run hf.co/peakji/steiner-32b-preview-gguf:Q4_K_M
The model is available in GGUF format on Hugging Face, making it compatible with various deployment options. This accessibility has been well-received by the community, particularly for those looking to experiment with advanced reasoning capabilities without OpenAI's associated costs and restrictions.
Technical Clarification
In response to community questions, the developer clarified that Steiner is not just an algorithm layered on top of an existing LLM, but rather a fine-tuned language model using a novel dataset and reinforcement learning rewards. While based on Qwen2.5-32B, it's specifically optimized for reasoning tasks, though not recommended as a direct replacement for general-purpose models like Llama.
Performance and Limitations
Community discussions have revealed both enthusiasm and pragmatic concerns about Steiner's capabilities:
- Benchmarks : The model shows a +5.56 improvement on GPQA-Diamond dataset, building upon Qwen2.5-32B's base performance of 49.49
- Reasoning Capabilities : According to the developer, the model can solve complex problems that other similarly-sized models struggle with, though this isn't always reflected in traditional benchmarks
- Current Limitations :
- Not optimized for multi-turn dialogues
- Predominantly works with English reasoning tokens
- Cannot yet reproduce o1's inference-time scaling capabilities
Future Potential
The community has expressed particular interest in Steiner's potential as an open-source alternative to OpenAI's o1, especially given the current landscape of commercial AI services. While the model hasn't yet achieved all of o1's capabilities, its open-source nature and active development make it a promising project for those interested in advanced reasoning systems.
The developer continues to work on improving the model's capabilities, with a particular focus on addressing the inference-time scaling challenge that has so far proved elusive.