Text Generation
Transformers
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English
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llama
minicpm
minicpm5
math
reasoning
theorem-proving
long-context
on-device
conversational
text-generation-inference
Instructions to use Caldalis/MiniCPM5-2B-Math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Caldalis/MiniCPM5-2B-Math with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Caldalis/MiniCPM5-2B-Math") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Caldalis/MiniCPM5-2B-Math") model = AutoModelForCausalLM.from_pretrained("Caldalis/MiniCPM5-2B-Math", 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Caldalis/MiniCPM5-2B-Math with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Caldalis/MiniCPM5-2B-Math" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Caldalis/MiniCPM5-2B-Math", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Caldalis/MiniCPM5-2B-Math
- SGLang
How to use Caldalis/MiniCPM5-2B-Math 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 "Caldalis/MiniCPM5-2B-Math" \ --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": "Caldalis/MiniCPM5-2B-Math", "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 "Caldalis/MiniCPM5-2B-Math" \ --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": "Caldalis/MiniCPM5-2B-Math", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Caldalis/MiniCPM5-2B-Math with Docker Model Runner:
docker model run hf.co/Caldalis/MiniCPM5-2B-Math
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<a href="https://github.com/Caldalis/MiniCPM-Math-harness" target="_blank">MiniCPM-Math harness</a> |
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<a href="https://huggingface.co/Caldalis/MiniCPM5-2B-Math-GGUF" target="_blank">GGUF</a> |
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<img src="https://huggingface.co/Caldalis/MiniCPM5-2B-Math/resolve/main/assets/banner.png" alt="MiniCPM5-2B-Math:面向数学与证明的 2.5B 模型,附带一个你自己就能跑起来的证明蜂群" width="100%" />
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<a href="https://github.com/Caldalis/MiniCPM-Math-harness" target="_blank">MiniCPM-Math harness</a> |
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<a href="https://github.com/Caldalis/MiniCPM-Math-harness" target="_blank">MiniCPM-Math Harness</a> |
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<img src="https://huggingface.co/Caldalis/MiniCPM5-2B-Math/resolve/main/assets/banner.png" alt="MiniCPM5-2B-Math: a 2.5B model for math and proofs, with a proof swarm you can run yourself" width="100%" />
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<a href="https://github.com/Caldalis/MiniCPM-Math-harness" target="_blank">MiniCPM-Math Harness</a> |
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