| --- |
| 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)) |
| |
