Instructions to use ViorikaAI-org/CalmaCatMath with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ViorikaAI-org/CalmaCatMath with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ViorikaAI-org/CalmaCatMath")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ViorikaAI-org/CalmaCatMath") model = AutoModelForCausalLM.from_pretrained("ViorikaAI-org/CalmaCatMath") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ViorikaAI-org/CalmaCatMath with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ViorikaAI-org/CalmaCatMath" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ViorikaAI-org/CalmaCatMath", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ViorikaAI-org/CalmaCatMath
- SGLang
How to use ViorikaAI-org/CalmaCatMath 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 "ViorikaAI-org/CalmaCatMath" \ --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": "ViorikaAI-org/CalmaCatMath", "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 "ViorikaAI-org/CalmaCatMath" \ --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": "ViorikaAI-org/CalmaCatMath", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ViorikaAI-org/CalmaCatMath with Docker Model Runner:
docker model run hf.co/ViorikaAI-org/CalmaCatMath
| license: mit | |
| language: | |
| - en | |
| - ru | |
| pipeline_tag: text-generation | |
| library_name: transformers | |
| tags: | |
| - text-generation-inference | |
| - gpt2 | |
| - gpt-2 | |
| # CalmaCatMath (19M) | |
| The first generation of the CalmaCatMath series. This is an ultra-compact language model with 19 million parameters, trained from scratch and tuned mainly to basic arithmetic. | |
| ### Model Features | |
| - **Size:** ~19M parameters — runs lightning-fast even on a potato. | |
| - **Vocabulary:** Custom Char tokenzier. | |
| - **Lore:** Basic NLP, Basic Arithmetic. |