Instructions to use bart1259/MiniCOTMath with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bart1259/MiniCOTMath with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bart1259/MiniCOTMath")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bart1259/MiniCOTMath") model = AutoModelForCausalLM.from_pretrained("bart1259/MiniCOTMath") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use bart1259/MiniCOTMath with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bart1259/MiniCOTMath" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bart1259/MiniCOTMath", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bart1259/MiniCOTMath
- SGLang
How to use bart1259/MiniCOTMath 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 "bart1259/MiniCOTMath" \ --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": "bart1259/MiniCOTMath", "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 "bart1259/MiniCOTMath" \ --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": "bart1259/MiniCOTMath", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bart1259/MiniCOTMath with Docker Model Runner:
docker model run hf.co/bart1259/MiniCOTMath
Upload folder using huggingface_hub
Browse files
README.md
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@@ -24,8 +24,7 @@ from transformers import pipeline
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pipe = pipeline(
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"text-generation", model="bart1259/MiniCOTMath"
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)
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print(pipe("Input: (5 + 5)
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", max_new_tokens=100)[0]["generated_text"])
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```
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Outputs:
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def __iter__(self):
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yield self
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prompt = "Input: (5 + 5)
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"
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tokenizer = AutoTokenizer.from_pretrained("bart1259/MiniCOTMath")
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model = AutoModelForCausalLM.from_pretrained("bart1259/MiniCOTMath").cuda()
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pipe = pipeline(
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"text-generation", model="bart1259/MiniCOTMath"
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)
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print(pipe("Input: (5 + 5)\n", max_new_tokens=100)[0]["generated_text"])
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```
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Outputs:
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def __iter__(self):
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yield self
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prompt = "Input: (5 + 5)\n"
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tokenizer = AutoTokenizer.from_pretrained("bart1259/MiniCOTMath")
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model = AutoModelForCausalLM.from_pretrained("bart1259/MiniCOTMath").cuda()
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