Update README.md

README.md

@@ -4,19 +4,22 @@ 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 `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.
+Дайте відповідь на завдання, починаючи з ключового слова "Відповідь:" та використовуючи лише наведені нижче варіанти. У якості відповіді наведіть лише літеру, що відповідає правильному варіанту. Якщо правильних відповідей декілька, то перерахуйте їх через ";".
 
 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.
@@ -28,11 +31,41 @@ PEFT 4bit tuning of `google/gemma-2-9b-it` on Ukrainian language and literature
 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>
+Відповідь: В;Д;А;Б<end_of_turn>
+```
+
+
+## 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 = "google/gemma-2-9b-it"
+
+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/gemma-2-it-zno-al", quantization_config=quantization_config, device_map="auto", torch_dtype=torch.float16, use_flash_attention_2=False)
+
+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,
+        eos_token_id=[101, 107]
+    )[0]))
 ```
 
 
@@ -42,188 +75,102 @@ PEFT 4bit tuning of `google/gemma-2-9b-it` on Ukrainian language and literature
 
 <!-- Provide a longer summary of what this model is. -->
 
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **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]
+- **Developed by:** NLP for UA
+- **Model type:** Gemma
+- **Language(s) (NLP):** Ukrainian (uk)
+- **License:** cc-by-nc-4.0
+- **Finetuned from model:** google/gemma-2-9b-it
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **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
 
-<!-- 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. -->
+The model can be used directly for generating correct answers to Ukrainian language and literature exam tasks. Input should follow the format shown in the example above.
 
-[More Information Needed]
-
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+The model could be fine-tuned further for other Ukrainian language tasks or integrated into educational applications.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model is specifically trained for Ukrainian exam tasks. It may not perform well on other languages or tasks.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+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
 
-<!-- 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]
+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
 
-<!-- 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. -->
-
-[More Information Needed]
+[More Information Needed - Link to Dataset Card and description]
 
 ### 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 -->
+- **Training regime:** 4-bit quantization
 
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+#### Speeds, Sizes, Times
 
 [More Information Needed]
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+[More Information Needed - Link to Dataset Card and description]
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
 [More Information Needed]
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
 [More Information Needed]
 
 ### Results
 
-[More Information Needed]
-
 #### 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]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
 [More Information Needed]
 
-#### Software
-
-[More Information Needed]
-
-## 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. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
+```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]
 
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
 ## Model Card Contact
 
 [More Information Needed]
+
 ### Framework versions
 
 - PEFT 0.14.0