初始化项目，由ModelHub XC社区提供模型

Model: nuinashco/gemma-3-1b-it-xlsum-ua-sft
Source: Original Platform

README.md

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+---
+language:
+- uk
+license: gemma
+base_model: unsloth/gemma-3-1b-it
+datasets:
+- csebuetnlp/xlsum
+tags:
+- summarization
+- ukrainian
+- unsloth
+- gemma3
+- sft
+metrics:
+- rouge
+pipeline_tag: text-generation
+---
+
+# gemma-3-1b-ua-safe-summarization
+
+Full fine-tune of [**unsloth/gemma-3-1b-it**](https://huggingface.co/unsloth/gemma-3-1b-it) on Ukrainian news summarization.  
+Trained with [Unsloth](https://github.com/unslothai/unsloth) + TRL SFT on the
+[**csebuetnlp/xlsum**](https://huggingface.co/datasets/csebuetnlp/xlsum) dataset
+(Ukrainian split of XL-Sum with safety filtering).
+
+## Training details
+
+| | |
+|---|---|
+| Base model | [unsloth/gemma-3-1b-it](https://huggingface.co/unsloth/gemma-3-1b-it) |
+| Dataset | [csebuetnlp/xlsum](https://huggingface.co/datasets/csebuetnlp/xlsum) |
+| Fine-tuning | Full SFT (no LoRA) |
+| Framework | [Unsloth](https://github.com/unslothai/unsloth) + TRL |
+| Epochs | ~1.48 (checkpoint-4000, best val ROUGE-L) |
+| Max seq length | 3072 |
+| Batch size | 8 per device |
+| Learning rate | 2e-5 (cosine decay, warmup 3 %) |
+| Precision | bfloat16 |
+| Optimizer | adamw_8bit |
+| Best eval ROUGE-L | **22.23** |
+
+Response-only masking was applied — loss is computed on the model turn only.
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "nuinashco/gemma-3-1b-it-xlsum-ua-sft"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, torch_dtype=torch.bfloat16, device_map="auto"
+)
+
+article = "Ваш текст новини тут..."
+prompt = [
+    {"role": "user", "content": f"Зроби короткий переказ наступного тексту:\n{article}"}
+]
+inputs = tokenizer.apply_chat_template(
+    prompt, add_generation_prompt=True, return_tensors="pt"
+).to(model.device)
+
+out = model.generate(inputs, max_new_tokens=128, do_sample=False)
+print(tokenizer.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
+```
+
+## Limitations
+
+- Trained for **Ukrainian** only; performance on other languages is undefined.
+- Inherits any biases present in the base model and training corpus.
+- Summaries may occasionally be factually inaccurate; always verify against the source.