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
```

Dmitriy-Zemskov commited on 16 days ago

Commit

c207d2c

verified ·

1 Parent(s): 4e16cc7

Update README.md

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@@ -15,28 +15,56 @@ library_name: transformers
 # CalmaCatCoder-280M
-[English](#english-version) | [Русская версия](#русская-версия)
 ---
-<a name="english-version"></a>
-## English Version
-**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
-* **Context / Format:** ChatML
 ---
-<a name="русская-версия"></a>
-## Русская версия
-**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
-* **Формат диалога:** ChatML

 # 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
+* **Inference Speed:** up to ~500 t/s
 ---
+## 📜 License
+Distributed under the **CalmaCat Public License (CCPL-1.0)**. See [LICENSE](./LICENSE) for details.
+---
+<details>
+<summary><b>🇷🇺 Нажмите, чтобы открыть описание на русском языке (Click to expand Russian description)</b></summary>
+<br>
+# 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
+* **Скорость инференса:** до ~500 t/s
+---
+## 📜 Лицензия
+Распространяется под кастомной открытой лицензией **CalmaCat Public License (CCPL-1.0)**.
+Полный текст см. в файле [LICENSE](./LICENSE).
+</details>
+---
+## 🚀 Quick Start / Быстрый запуск
+Check out the `llama.cpp` CLI execution command in the repository files or run it locally using your preferred GGUF runner.