Instructions to use MathGPT/DinoSpark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MathGPT/DinoSpark with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MathGPT/DinoSpark")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MathGPT/DinoSpark", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MathGPT/DinoSpark with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MathGPT/DinoSpark" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MathGPT/DinoSpark", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MathGPT/DinoSpark
- SGLang
How to use MathGPT/DinoSpark 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 "MathGPT/DinoSpark" \ --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": "MathGPT/DinoSpark", "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 "MathGPT/DinoSpark" \ --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": "MathGPT/DinoSpark", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MathGPT/DinoSpark with Docker Model Runner:
docker model run hf.co/MathGPT/DinoSpark
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Download README.md from MathGPT/DinoSpark: direct link, hf CLI and curl.
- Browser
- Download file 891 Bytes
-
https://huggingface.co/MathGPT/DinoSpark/resolve/main/README.md
- Command line
-
hf download hf://MathGPT/DinoSpark/README.md
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curl -L -o README.md https://huggingface.co/MathGPT/DinoSpark/resolve/main/README.md
891 Bytes
| license: apache-2.0 | |
| datasets: | |
| - open-r1/OpenR1-Math-220k | |
| language: | |
| - en | |
| base_model: | |
| - ashabrawy/bert-base-uncased_finetuning_distillation | |
| pipeline_tag: text-generation | |
| metrics: | |
| - accuracy | |
| library_name: transformers | |
| new_version: ashabrawy/bert-base-uncased_finetuning_distillation | |
| tensor_type: F32 | |
| # Model Card for Model ID | |
| <!-- Provide a quick summary of what the model is/does. --> | |
| This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). | |
| ## Model Details | |
| ### Model Description | |
| <!-- Provide a longer summary of what this model is. --> | |
| - **Developed by:** A team of 2 teenagers | |
| - **Model type:** Text generation | |
| - **Language(s) (NLP):** english | |
| - **License:** apache 2 | |
| - **Finetuned from model DeepSeek-R1** |