Show HN: Steiner – An open-source reasoning model inspired by OpenAI o1 (medium.com)
Steiner is a series of reasoning models trained on synthetic data using reinforcement learning. These models can explore multiple reasoning paths in an autoregressive manner during inference and autonomously verify or backtrack when necessary, enabling a linear traversal of the implicit search tree.
Blog: https://medium.com/@peakji/a-small-step-towards-reproducing-...
Hugging Face: https://huggingface.co/collections/peakji/steiner-preview-67...
21 comments
[ 5.0 ms ] story [ 64.1 ms ] threadcan you compare with just qwen 32b with CoT?
More importantly, I highly recommend to try these out firsthand (not only Steiner, but all reasoning models). You'll find that these reasoning models can solve many problems that other models with the same parameter size cannot handle. The existing benchmarks may not reflect this well, as I mentioned in the article:
"... automated evaluation benchmarks, which are primarily composed of multiple-choice questions and may not fully reflect the capabilities of reasoning models. During the training phase, reasoning models are encouraged to engage in open-ended exploration of problems, whereas multiple-choice questions operate under the premise that "the correct answer must be among the options." This makes it evident that verifying options one by one is a more efficient approach. In fact, existing large language models have, consciously or unconsciously, mastered this technique, regardless of whether special prompts are used. Ultimately, it is this misalignment between automated evaluation and genuine reasoning requirements that makes me believe it is essential to open-source the model for real human evaluation and feedback."
Im wondering if we can abstract chain of thought further down into the computation levels to replace a lot of matrix multiply. Like smaller transformers with less parameters and more selection of which transformer to use through search.
Is this a fined tuned LLM, for example drop in replacement for Llama etc.
Or is it some algorithm on top of an LLM, doing some chain of reasoning?
(realistically speaking, experts tend to know less about the blog hosting ecosystem the more they know about their domain)
I haven't personally used Ollama Modelfile, but I think it should be relatively easy to convert from GGUF?
ollama run hf.co/{username}/{repository}
Example: ollama run hf.co/peakji/steiner-32b-preview-gguf:Q4_K_M
Source: https://huggingface.co/docs/hub/en/ollama