| | --- |
| | language: |
| | - en |
| | license: apache-2.0 |
| | tags: |
| | - math |
| | - reasoning |
| | - mathematics |
| | - causal-lm |
| | - text-generation |
| | library_name: transformers |
| | pipeline_tag: text-generation |
| | model_name: Minnow-Math-2B |
| | --- |
| | |
| | # 🐟 Minnow-Math-2B |
| |
|
| | **Minnow-Math-2B** is a 2B-parameter language model by **Kitefish**, focused on mathematical reasoning, symbolic understanding, and structured problem solving. |
| |
|
| | This is an early release and part of our ongoing effort to build strong, efficient models for reasoning-heavy tasks. |
| |
|
| | --- |
| |
|
| | ## ✨ What this model is good at |
| |
|
| | - Basic to intermediate **math problem solving** |
| | - **Step-by-step reasoning** for equations and word problems |
| | - Understanding **mathematical symbols and structure** |
| | - Educational and experimentation use cases |
| |
|
| | --- |
| |
|
| | ## 🚀 Quick start |
| |
|
| | ```python |
| | from transformers import AutoTokenizer, AutoModelForCausalLM |
| | |
| | tokenizer = AutoTokenizer.from_pretrained("kitefish/Minnow-Math-2B") |
| | model = AutoModelForCausalLM.from_pretrained( |
| | "kitefish/Minnow-Math-2B", |
| | torch_dtype="auto", |
| | device_map="auto" |
| | ) |
| | |
| | prompt = "Solve: 2x + 5 = 13" |
| | inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
| | |
| | outputs = model.generate(**inputs, max_new_tokens=100) |
| | print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
| | |
