Instructions to use ViorikaAI-org/CalmaCatMath with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

CalmaCatMath / README.md

Dmitriy-Zemskov

Update README.md

36fe8b9 verified about 22 hours ago

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metadata

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.