Add NLPForUA__Llama-3.1-8B-Instruct-zno-cot-with-topic

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NLPForUA__Llama-3.1-8B-Instruct-zno-cot-with-topic/README.md

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+---
+base_model: meta-llama/Llama-3.1-8B-Instruct
+library_name: peft
+license: cc-by-nc-4.0
+language:
+- uk
+pipeline_tag: text-generation
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+**This model is CC BY NC 4.0 (allowing only non-commercial use) and should not be used outside of research purposes.**
+
+Presented in [Empowering Smaller Models: Tuning LLaMA and Gemma with Chain-of-Thought for Ukrainian Exam Tasks (arXiv:2503.13988)](https://arxiv.org/abs/2503.13988)
+
+PEFT 4bit tuning of meta-llama/Llama-3.1-8B-Instruct on Ukrainian language and literature tasks of ZNO (EIE) & NMT dataset to generate step-by-step solution with task topic:
+
+```
+<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+
+Cutting Knowledge Date: December 2023
+Today Date: 26 Jul 2024
+
+<|eot_id|><|start_header_id|>user<|end_header_id|>
+
+Дайте розгорнуту відповідь на завдання, починаючи з ключового слова "Відповідь:" та використовуючи лише наведені нижче варіанти.
+
+Завдання: З’ясуйте, якими частинами мови є виділені слова в реченні (цифра позначає наступне слово).
+Сучасна людина, щоб бути (1)успішною, має вчитися (2)впродовж (3)усього життя, (4)опановуючи нові галузі знань.
+
+Варіанти відповіді:
+А – займенник
+Б – прикметник
+В – форма дієслова (дієприкметник)
+Г – форма дієслова (дієприслівник)
+Д – прийменник<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+Відповідь:
+ТЕМА: Морфологія. Частини мови
+Завдання перевіряє ваше вміння правильно визначати частини мови.
+Необхідно бути дуже уважним, тому що частиномовна приналежність конкретного слова часто залежить від контексту. Проаналізуймо кожне із запропонованих слів.
+Слово успішна відповідає на питання яка? Отже, це прикметник.
+Слово упродовж відповідає на питання з якою частиною речення поєднується? Отже, це прийменник.
+Слово усесь відповідає на питання який? Отже, це займенник.
+А слово опановуючи відповідає на питання що роблячи? Отже, це дієприслівник.
+Відповідь: БДВА.<|eot_id|>
+```
+
+
+## Inference code
+
+```
+import torch
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.float16, # computation in fp16
+    bnb_4bit_use_double_quant=True,       # enables double quantization for better accuracy
+    bnb_4bit_quant_type="nf4"             # choose "nf4" (normal float4) or other types as supported
+)
+
+base_model = "unsloth/Llama-3.1-8B-Instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(base_model, max_sequence_length=3072, model_max_length=3072)
+model_base = AutoModelForCausalLM.from_pretrained(base_model, quantization_config=quantization_config, device_map="auto", torch_dtype=torch.float16, use_flash_attention_2=False)
+model = PeftModel.from_pretrained(model_base, "NLPForUA/Llama-3.1-8B-Instruct-zno-cot-with-topic", quantization_config=quantization_config, device_map="auto", torch_dtype=torch.float16, use_flash_attention_2=False)
+
+input_text = """
+Дайте розгорнуту відповідь на завдання, починаючи з ключового слова "Відповідь:".
+
+Завдання: x / 2 + 4 = 6.5, знайдіть x. 
+"""
+
+messages = [{"role": "user", "content": input_text}]
+
+inputs = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt",
+    add_special_tokens=False,
+).to("cuda")
+
+print(tokenizer.decode(
+    model.generate(
+        input_ids=inputs,
+        max_new_tokens=1024,
+        use_cache=True,
+        temperature=0.0,
+        do_sample=False,
+        repetition_penalty=1.0,
+        pad_token_id=tokenizer.eos_token_id
+    )[0]))
+```
+
+Output:
+```
+<|start_header_id|>assistant<|end_header_id|>
+
+Відповідь:
+ТЕМА: Математичне програмування. Визначення змінних, використовуючи їх у виразах.
+Завдання перевіряє ваше вміння визначати змінні, використовуючи їх у виразах.
+Проаналізуймо запропоноване завдання.
+Змінну х ми визначаємо, виконуючи ряд операцій:
+1. відберемо ліву частину (АЛГОРІТМ ЗМІННОЇ Х)
+2. відберемо праву частину (рівна 6,5)
+3. виконаємо операцію з лівої та правої частини (х/2 + 4 = 6,5)
+4. відберемо змінну х (х/2 = 6,5 - 4 = 2,5)
+5. виконайте операцію з змінною х (х = 2,5 * 2 = 5)
+Відповідь: 5.<|eot_id|>
+```
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+- **Developed by:** NLP for UA
+- **Model type:** LLaMA
+- **Language(s) (NLP):** Ukrainian (uk)
+- **License:** cc-by-nc-4.0
+- **Finetuned from model:** unsloth/Llama-3.1-8B-Instruct
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [github.com/NLPForUA/ZNO](https://github.com/NLPForUA/ZNO)
+- **Paper:** [Empowering Smaller Models: Tuning LLaMA and Gemma with Chain-of-Thought for Ukrainian Exam Tasks (arXiv:2503.13988)](https://arxiv.org/abs/2503.13988)
+
+## Uses
+
+### Direct Use
+
+The model can be used directly for generating step-by-step solutions to Ukrainian language and literature exam tasks. Input should follow the format shown in the example above.
+
+### Downstream Use
+
+The model could be fine-tuned further for other Ukrainian language tasks or integrated into educational applications.
+
+### Out-of-Scope Use
+
+This model is specifically trained for Ukrainian exam tasks. It may not perform well on other languages or tasks.
+
+## Bias, Risks, and Limitations
+
+The model may exhibit biases present in the training data. It is crucial to critically evaluate its outputs and be aware of potential inaccuracies. Further analysis is needed to fully characterize biases and limitations.
+
+### Recommendations
+
+Users should be aware of the potential biases and limitations of the model and use its output critically. Further evaluation is needed to fully assess the model's capabilities and limitations.
+
+## Training Details
+
+### Training Data
+
+[More Information Needed - Link to Dataset Card and description]
+
+### Training Procedure
+
+[More Information Needed]
+
+#### Training Hyperparameters
+
+- **Training regime:** 4-bit quantization
+
+#### Speeds, Sizes, Times
+
+[More Information Needed]
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+[More Information Needed - Link to Dataset Card and description]
+
+#### Factors
+
+[More Information Needed]
+
+#### Metrics
+
+[More Information Needed]
+
+### Results
+
+#### Summary
+
+[More Information Needed]
+
+## Citation
+
+**BibTeX:**
+
+```bibtex
+@article{EmpoweringSmallerModels,
+  author = {Mykyta Syromiatnikov, Victoria Ruvinskaya, and Nataliia Komleva},
+  title = {Empowering Smaller Models: Tuning LLaMA and Gemma with Chain-of-Thought for Ukrainian Exam Tasks},
+  journal = {arXiv preprint arXiv:2503.13988},
+  year = {2025}
+}
+```
+
+**APA:**
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+
+### Framework versions
+
+- PEFT 0.14.0

NLPForUA__Llama-3.1-8B-Instruct-zno-cot-with-topic/adapter_config.json

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NLPForUA__Llama-3.1-8B-Instruct-zno-cot-with-topic/trainer_state.json

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