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 +2 -2
- hyperparameters.json +1 -0
- tokenizer.json +28 -2
README.md
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Chain of Thought (CoT) transformer model trained to do multi-step integer arithmetic.
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Model details:
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- **Vocabulary Size**:
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- **Layer Count**: 8
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- **Attention Head Count**: 4
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- **Residual Stream Size**: 256
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- **Context Length**: 256
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- **Tokens Trained on**: 419,
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Training Score During Training
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Chain of Thought (CoT) transformer model trained to do multi-step integer arithmetic.
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Model details:
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- **Vocabulary Size**: 40 (Character Tokenization)
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- **Layer Count**: 8
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- **Attention Head Count**: 4
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- **Residual Stream Size**: 256
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- **Context Length**: 256
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- **Tokens Trained on**: 419,612,160
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Training Score During Training
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hyperparameters.json
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{"MIN_DIFFICULTY": 2, "MAX_DIFFICULTY": 4, "TRAINING_SAMPLES": 3000000, "CONTEXT_LENGTH": 256, "RESIDUAL_EMBEDDING_SIZE": 256, "MLP_EMBEDDING_SIZE": 1024, "NUM_ATTENTION_HEADS": 4, "NUM_LAYERS": 8, "VOCAB_SIZE": 40, "TOTAL_TOKENS": 419612160}
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tokenizer.json
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"t": 36,
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"u": 37,
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"Ċ": 38,
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"Ġ": 39
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"merges": [
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}
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"t": 36,
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"u": 37,
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"Ċ": 38,
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"Ġ": 39,
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"Ġ-": 40,
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"(-": 41,
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"Ġ1": 42,
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"St": 43,
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"ep": 44
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"merges": [
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"Ġ",
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