Text Generation
Transformers
Safetensors
English
llama
math
reasoning
mathematics
causal-lm
text-generation-inference
Instructions to use KiteFishAI/Minnow-Math-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KiteFishAI/Minnow-Math-2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="KiteFishAI/Minnow-Math-2B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("KiteFishAI/Minnow-Math-2B") model = AutoModelForCausalLM.from_pretrained("KiteFishAI/Minnow-Math-2B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use KiteFishAI/Minnow-Math-2B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "KiteFishAI/Minnow-Math-2B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KiteFishAI/Minnow-Math-2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/KiteFishAI/Minnow-Math-2B
- SGLang
How to use KiteFishAI/Minnow-Math-2B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "KiteFishAI/Minnow-Math-2B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KiteFishAI/Minnow-Math-2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "KiteFishAI/Minnow-Math-2B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KiteFishAI/Minnow-Math-2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use KiteFishAI/Minnow-Math-2B with Docker Model Runner:
docker model run hf.co/KiteFishAI/Minnow-Math-2B
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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))
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