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

Libraries

How to use ViorikaAI-org/CalmaCatCoder with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ViorikaAI-org/CalmaCatCoder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ViorikaAI-org/CalmaCatCoder")
model = AutoModelForCausalLM.from_pretrained("ViorikaAI-org/CalmaCatCoder", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ViorikaAI-org/CalmaCatCoder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ViorikaAI-org/CalmaCatCoder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ViorikaAI-org/CalmaCatCoder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ViorikaAI-org/CalmaCatCoder

SGLang

How to use ViorikaAI-org/CalmaCatCoder 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/CalmaCatCoder" \
    --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": "ViorikaAI-org/CalmaCatCoder",
		"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 "ViorikaAI-org/CalmaCatCoder" \
        --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": "ViorikaAI-org/CalmaCatCoder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ViorikaAI-org/CalmaCatCoder with Docker Model Runner:
```
docker model run hf.co/ViorikaAI-org/CalmaCatCoder
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

CalmaCatCoder-280M

CalmaCatCoder-280M is a compact and ultra-fast 280M language model trained from scratch for Python code generation.

⚡ Specs

Architecture: Transformer / Causal LM (Qwen2-like)
Parameters: ~280M
Language: English (Code)
Format: ChatML
Context: 4096

📜 License

Distributed under the CalmaCat Public License (CCPL-1.0). See LICENSE for details.

🇷🇺 Нажмите, чтобы открыть описание на русском языке (Click to expand Russian description)

CalmaCatCoder-280M

CalmaCatCoder-280M — компактная и ультрабыстрая языковая модель на 280 млн параметров, обученная с нуля для генерации кода на Python.

⚡ Характеристики

Архитектура: Transformer / Causal LM (Qwen2-like)
Объём параметров: ~280 млн
Основной язык: English (Код)
Формат диалога: ChatML
Контекст: 4096

📜 Лицензия

Распространяется под кастомной открытой лицензией CalmaCat Public License (CCPL-1.0). Полный текст см. в файле LICENSE.

🚀 Quick Start / Быстрый запуск

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ViorikaAI-org/CalmaCatCoder"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

prompt = "<|im_start|>user\nWrite a Python function for binary search.<|im_end|>\n<|im_start|>assistant\n"

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.6,
    top_p=0.9,
    repetition_penalty=1.25,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=False))

Downloads last month: -

Safetensors

Model size

0.3B params

Tensor type

F32

Model tree for ViorikaAI-org/CalmaCatCoder

Quantizations

1 model