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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
**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](./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
* **Контекст:** 4096
---
## 📜 Лицензия
Распространяется под кастомной открытой лицензией **CalmaCat Public License (CCPL-1.0)**.
Полный текст см. в файле [LICENSE](./LICENSE).
</details>
---
## 🚀 Quick Start / Быстрый запуск
```python
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))
``` |