NLPForUA
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gemma-2-it-zno-al

@@ -1,229 +1,145 @@
 ---
 base_model: google/gemma-2-9b-it
-library_name: peft
-license: cc-by-nc-4.0
 language:
 - uk
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-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 `google/gemma-2-9b-it` on Ukrainian language and literature tasks of ZNO (EIE) & NMT dataset to generate correct answer letter:
-```
-<bos><start_of_turn>user
-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<end_of_turn>
-<start_of_turn>model
-Відповідь:
-1 - В
-2 - Д
-3 - А
-4 - Г<end_of_turn>
-```
 ## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
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-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 ## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 ### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
 ### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
 ## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
 ### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 ## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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 ## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
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-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
 ## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
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-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
 ### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## Model Card Authors [optional]
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 ## Model Card Contact
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-### Framework versions
-- PEFT 0.14.0

 ---
 base_model: google/gemma-2-9b-it
 language:
 - uk
+library_name: peft
+license: cc-by-nc-4.0
+pipeline_tag: text-generation
 ---
+# Empowering Smaller Models: Tuning Gemma for Ukrainian Exam Tasks
+This model, 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), is a 4-bit quantized PEFT adaptation of `google/gemma-2-9b-it` fine-tuned on Ukrainian language and literature exam tasks from the ZNO (EIE) & NMT datasets.  It's designed to generate the correct answer letter for multiple-choice questions.  The model takes a prompt consisting of the question and answer choices and outputs the letter corresponding to the correct answer.
 ## Model Details
+This model was developed using the PEFT library for efficient parameter-efficient fine-tuning.
+- **Model type:**  Causal Language Model
+- **Language(s) (NLP):** Ukrainian (uk)
+- **License:** CC-BY-NC-4.0
+- **Finetuned from model:** `google/gemma-2-9b-it`
+### Model Sources
+- **Repository:** https://github.com/AndriyAntypenko/UKR-GEC-LLM
+- **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 the letter corresponding to the correct answer for Ukrainian language and literature exam questions, formatted as shown in the example below.
+### Downstream Use
+This model could be integrated into educational applications or question-answering systems focused on Ukrainian language and literature.
 ### Out-of-Scope Use
+This model is specifically trained for Ukrainian exam tasks and should not be used for other tasks or languages.  Its performance on other domains is not guaranteed and may be unreliable.
 ## Bias, Risks, and Limitations
+The model's performance is heavily dependent on the quality and characteristics of the training data (ZNO (EIE) & NMT datasets). Biases present in this data may be reflected in the model's output.  The model's accuracy is limited to the specific types of questions present in the training data.  Over-reliance on this model for high-stakes decisions without proper human oversight is strongly discouraged.
 ### Recommendations
+Users should be cautious about the model's potential biases and limitations.  Human review of the model's output is crucial, especially for high-stakes applications.  Further research is needed to fully understand and mitigate potential biases.
 ## How to Get Started with the Model
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
+import torch
+model_id = "ybelonogov/gemma-zno-eie" # Replace with the actual Hugging Face model ID
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)
+prompt = """<bos><start_of_turn>user
+Дайте розгорнуту відповідь на завдання, починаючи з ключового слова "Відповідь:" та використовуючи лише наведені нижче варіанти.
+Завдання: ... [Your Question Here] ...
+Варіанти відповіді:
+А – ...
+Б – ...
+В – ...
+Г – ...
+Д – ...<end_of_turn>"""
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+generation_config = GenerationConfig(
+    temperature=0.1,
+    max_new_tokens=512,
+    do_sample=False
+)
+outputs = model.generate(inputs["input_ids"], **generation_config.to_dict())
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(generated_text)
+```
+## Training Details
+### Training Data
+[More information needed -  link to dataset card or description of ZNO (EIE) & NMT datasets]
+### Training Procedure
+The model was fine-tuned using the PEFT library's QLoRA technique.  Further details about the training hyperparameters are required.
+#### Training Hyperparameters
+- **Training regime:** 4-bit quantization
 ## Evaluation
+[More information needed on evaluation metrics, datasets, etc.]
 ## Environmental Impact
+[More information needed]
+## Technical Specifications
 ### Model Architecture and Objective
+[More information needed]
+### Compute Infrastructure
+[More information needed]
+## Citation
+[More information about citation details needed]
+## Glossary
+[Add glossary if needed]
+## More Information
+[Add more information if needed]
+## Model Card Authors
+[Add author information]
 ## Model Card Contact
+[Add contact information]