--- license: apache-2.0 datasets: - Sweaterdog/Smol-reason2.1 language: - en base_model: - unsloth/Qwen2.5-3B-Instruct-bnb-4bit --- # 🧠Smol-reason2.1🧠 This is my third GRPO reasoning model, I was exploring fine tuning on my own hardware, and found it to work with 3B models. System prompt: ``` You are a reasoning LLM named Smol-reason2.1, developed by Sweaterdog. Respond in the following format: ...reason in long recursive loops here... ...answer here... Start your response with ``` And in accordance to the output format, the model responds like this: ``` Okay, lets break down the users issue. ...more reasoning... Therefore x should be the answer X is the answer because... ``` # Features ## Flexible reasoning You can modify the system prompt to change the way the model reasons, by default, it is told to reason about code snippets, which I found works best for everything. ## Logical reasoning This is the first model I have seen which can answer "The Mango Puzzle", which goes like this: ``` If I give you 15 mangoes, and then you give 14 away, then recieve 60 more mangoes, how many mangoes did you not sell? ``` The correct answer is `75 Mangoes`, most LLMs take "Give Away" as a form of sale, so they typically say `61 Mangoes` ## Code reasoning This model is capable of thinking about how to design complex code problems before tackling the entire file. ## Mathematical reasoning This model is capable of breaking down math equations, and checking its own work before responding with an answer. ## Medical reasoning This model is capable of taking in symptoms of a disease, as well as the patients condition, and properly prescribing a diagnosis. # Design This model was trained off of Qwen2.5 3B and trained on a dataset I put together comprised of Coding, Healthcare, and Math To be specific, this model was trained off of Smol-reason2, for longer and on a larger dataset of reasoning data from DeepSeek-R1 This model has RoPE scaling up to `65536`, and the Q8_0 model can fit on a single GPU with the full context length.