Add pipeline tag, library name, and paper link

This PR adds the `pipeline_tag`, `library_name`, and a link to the Hugging Face paper page to the model card. This improves discoverability and provides more complete information about the model.

README.md

@@ -1,25 +1,26 @@
 ---
-license: mit
 base_model:
 - ai-sage/GigaChat-20B-A3B-instruct
 language:
 - ru
 - en
+license: mit
+pipeline_tag: text-generation
+library_name: transformers
 ---
-# GigaChat-20B-A3B-instruct bf16
 
-Диалоговая модель из семейства моделей GigaChat, основная на [ai-sage/GigaChat-20B-A3B-instruct](https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct). Поддерживает контекст в 131 тысячу токенов.
+# GigaChat-20B-A3B-instruct bf16
 
-Больше подробностей в [хабр статье](https://habr.com/en/companies/sberdevices/articles/865996/) и в карточке оригинальной instruct модели.
+This model is part of the GigaChat family of Russian LLMs, based on [ai-sage/GigaChat-20B-A3B-instruct](https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct). It supports a context length of 131,000 tokens.
 
+More details are available in [this habr article](https://habr.com/en/companies/sberdevices/articles/865996/) and the original instruct model card. The model was presented in [GigaChat Family: Efficient Russian Language Modeling Through Mixture of Experts Architecture](https://huggingface.co/papers/2506.09440).
 
-## Пример использования через transformers
+## Example Usage with Transformers
 
 ```bash
 pip install --upgrade transformers torch accelerate bitsandbytes
 ```
 
-
 ```python
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
@@ -37,5 +38,4 @@ outputs = model.generate(input_tensor.to(model.device))
 
 result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=False)
 print(result)
-```
-
+```