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 12 days ago

Commit

4e16cc7

verified ·

1 Parent(s): ad62201

Update README.md

Browse files

Files changed (1) hide show

README.md +38 -1

README.md CHANGED Viewed

@@ -1,5 +1,42 @@
 ---
 license: other
-license_name: ccpl-1
 license_link: LICENSE
 ---

 ---
 license: other
+license_name: ccpl-1.0
 license_link: LICENSE
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- code
+- qwen2
+- gguf
+- 280m
+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