Text Generation
Transformers
Safetensors
English
mixtral
mathematics
reasoning
algebra
arithmetic
geometry
calculus
statistics
number-theory
ai
adaption-labs
autoscientist
lora
education
stem
conversational
text-generation-inference
Instructions to use Charley890/AdaptiveMath with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Charley890/AdaptiveMath with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Charley890/AdaptiveMath") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Charley890/AdaptiveMath") model = AutoModelForCausalLM.from_pretrained("Charley890/AdaptiveMath", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Charley890/AdaptiveMath with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Charley890/AdaptiveMath" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Charley890/AdaptiveMath", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Charley890/AdaptiveMath
- SGLang
How to use Charley890/AdaptiveMath 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 "Charley890/AdaptiveMath" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Charley890/AdaptiveMath", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Charley890/AdaptiveMath" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Charley890/AdaptiveMath", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Charley890/AdaptiveMath with Docker Model Runner:
docker model run hf.co/Charley890/AdaptiveMath
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| Volume of Sphere | $V=\frac43\pi r^3$ |
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| Surface Area of Sphere | $A=4\pi r^2$ |
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| Volume of Cylinder | $V=\pi r^2h$ |
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| Volume of Cone | $V=\frac13\pi r^2h$ |
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| Euler's Formula | $e^{ix}=\cos x+i\sin x$ |
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| Euler's Identity | $e^{i\pi}+1=0$ |
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| Bayes' Theorem | $P(A\mid B)=\frac{P(B\mid A)P(A)}{P(B)}$ |
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| Arithmetic Mean | $\bar{x}=\frac{x_1+\cdots+x_n}{n}$ |
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| Geometric Mean | $GM=\sqrt[n]{x_1x_2\cdots x_n}$ |
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| Harmonic Mean | $HM=\frac{n}{\sum\frac1{x_i}}$ |
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| Standard Deviation | $\sigma=\sqrt{\frac{\sum(x-\mu)^2}{N}}$ |
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```text
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Difference of squares
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| Volume of Sphere | $V=\frac43\pi r^3$ |
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