Instructions to use Dmitriy-Zemskov/CalmaCatLM-1.5-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dmitriy-Zemskov/CalmaCatLM-1.5-mini with Transformers:

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

pipe = pipeline("text-generation", model="Dmitriy-Zemskov/CalmaCatLM-1.5-mini")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Dmitriy-Zemskov/CalmaCatLM-1.5-mini", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Dmitriy-Zemskov/CalmaCatLM-1.5-mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dmitriy-Zemskov/CalmaCatLM-1.5-mini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dmitriy-Zemskov/CalmaCatLM-1.5-mini",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Dmitriy-Zemskov/CalmaCatLM-1.5-mini

SGLang

How to use Dmitriy-Zemskov/CalmaCatLM-1.5-mini 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 "Dmitriy-Zemskov/CalmaCatLM-1.5-mini" \
    --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": "Dmitriy-Zemskov/CalmaCatLM-1.5-mini",
		"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 "Dmitriy-Zemskov/CalmaCatLM-1.5-mini" \
        --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": "Dmitriy-Zemskov/CalmaCatLM-1.5-mini",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Dmitriy-Zemskov/CalmaCatLM-1.5-mini with Docker Model Runner:
```
docker model run hf.co/Dmitriy-Zemskov/CalmaCatLM-1.5-mini
```

CalmaCatLM-1.5-mini / README.md

Dmitriy

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	---
	language:
	- en
	- ru
	license: mit
	tags:
	- causal-lm
	- text-generation
	- chatbot
	- experimental
	model_type: gpt
	datasets:
	...
	library_name: transformers
	---

	(discord https://discord.gg/DUzP7CXqJt , https://discord.gg/jzwR7jFfSB)
	Website: https://calmacatai.draklor.ru

	## License

	This model is licensed under the MIT License.


	# CalmaCatLM-1.5-mini

	🚧 Experimental Under-Training Model (~12M parameters) based on a custom 12-layer/12-head Transformer architecture.
	Primarily supports English 🇬🇧. This is my third model.

	## 📖 Description
	CalmaCatLM is an experimental generative language model designed for text generation and dialogue tasks.
	The main goal of this project is to test the full pipeline: from implementing the architecture and training from scratch to uploading models to the Hugging Face Hub.

	## ⚙️ Model Details
	- Architecture: Custom Transformer Decoder (6 layers, 6 attention heads)
	- Model size: ~12M parameters #
	- Training Approach: Pre-trained from scratch on My dataset
	- Languages: Primarily Russian
	- License: MIT

	## 🏋️ Training Details
	- Dataset: `My`
	- Hardware: Single AMD RX 7700 XT (12GB VRAM)
	- Training Status: Very early checkpoint (Under-trained)
	- Epochs: 100
	- Batch size: 32
	- Optimizer: AdamW, lr = 3e-4
	- Max sequence length: 128 tokens