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-library_name: transformers
-tags: []
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+# Model Card for olsi8/gemma-3-4b-it-shqip-v1
 
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
 
+### Model Details
 
+**Model Description**
 
-## Model Details
+This model, identified as olsi8/gemma-3-4b-it-shqip-v1, is a 🤗 Transformers model hosted on the Hugging Face Hub. It represents a fine-tuned iteration of the gemma-3-4b-it model, specifically adapted for the Albanian language (Shqip). The primary purpose of this model is text generation, leveraging the underlying capabilities of the Gemma architecture. This model card provides an overview of its development, intended uses, and other relevant details.
 
-### Model Description
+**Developed by:**
+The model was developed by the Hugging Face user olsi8.
 
-<!-- Provide a longer summary of what this model is. -->
+**Funded by (optional):**
+Information regarding funding for this specific fine-tuning effort is not explicitly provided.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+**Shared by (optional):**
+The model is shared by the Hugging Face user olsi8.
 
-- **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]
+**Model type:**
+This is a large language model (LLM) based on the Gemma architecture. It is designed for text generation tasks and has been fine-tuned from the `gemma-3-4b-it` model. The tags on its Hugging Face page include "Image-Text-to-Text", "Transformers", "gemma3", and "text-generation-inference", indicating its capabilities and framework.
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
+**Language(s) (NLP):**
+The primary language supported by this model is Albanian (Shqip). Given its base model, it may retain some capabilities in English, though its fine-tuning focus is on Albanian.
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+**License:**
+The license for this specific fine-tuned model is not explicitly stated on its Hugging Face page. It is likely to inherit the license of the base model, `gemma-3-4b-it`, which is typically governed by the Gemma Terms of Use. Users should verify the licensing terms before use.
 
-## Uses
+**Finetuned from model (optional):**
+This model was fine-tuned from `google/gemma-3-4b-it`.
 
-<!-- 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]
+### Model Sources [optional]
 
-### Downstream Use [optional]
+**Repository:**
+The model is hosted on the Hugging Face Model Hub. The repository can be found at [https://huggingface.co/olsi8/gemma-3-4b-it-shqip-v1](https://huggingface.co/olsi8/gemma-3-4b-it-shqip-v1).
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+**Paper [optional]:**
+There is no specific paper associated with this fine-tuned model. However, for information on the base Gemma models, users can refer to the relevant Google publications.
 
-[More Information Needed]
+**Demo [optional]:**
+No specific demo is provided for this model on its Hugging Face page.
 
-### Out-of-Scope Use
+### Uses
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+**Direct Use:**
+This model is intended for direct use in generating text in the Albanian language. It can be employed for tasks such as content creation, translation assistance (from other languages to Albanian, with caution), and as a foundational tool for further fine-tuning on more specific Albanian NLP tasks. Due to its proof-of-concept nature, extensive testing is recommended before deployment in production environments.
 
-[More Information Needed]
+**Downstream Use [optional]:**
+The model can serve as a base for further fine-tuning on specialized Albanian language datasets for tasks like sentiment analysis, question answering, or domain-specific text generation.
 
-## Bias, Risks, and Limitations
+**Out-of-Scope Use:**
+This model is not intended for generating harmful, biased, or misleading content. It should not be used for critical decision-making without human oversight, especially given its current stage as a proof-of-concept. Use in languages other than Albanian is not its primary design and may yield suboptimal results.
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+### Bias, Risks, and Limitations
 
-[More Information Needed]
+As with all large language models, olsi8/gemma-3-4b-it-shqip-v1 may inherit biases present in its training data, which includes books and the `olsi8/albanian-lang-gemma-format` dataset. These biases could manifest in the generated text. The model's performance is limited by the scope and quality of its training data; it may generate incorrect or nonsensical information (hallucinations). Given that it is a proof-of-concept, its robustness and generalization capabilities might be limited compared to more extensively trained models. The accuracy of 0.77 on the `olsi8/albanian-lang-gemma-format` dataset indicates that while it has learned the task to a degree, there is still room for improvement and potential for errors.
 
 ### 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. -->
-
-[More Information Needed]
-
-### Training Procedure
+Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. It is strongly recommended to thoroughly evaluate the model's outputs for any specific application. Human oversight is crucial when using the model for any sensitive tasks. Users should also note that this model is considered a proof of concept and has been superseded by newer versions or alternative approaches, as indicated by its deprecated status. For production use, exploring more mature and extensively evaluated models is advised.
 
-<!-- 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]
+### How to Get Started with the Model
 
+To get started with the `olsi8/gemma-3-4b-it-shqip-v1` model, you can use the Hugging Face Transformers library. Below is an example of how to load the model and tokenizer and generate text. Ensure you have the `transformers` and `torch` libraries installed.
 
-#### Training Hyperparameters
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+model_id = "olsi8/gemma-3-4b-it-shqip-v1"
 
-#### Speeds, Sizes, Times [optional]
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+# Prepare your input prompt (example in Albanian)
+input_text = "Përshëndetje, si mund të të ndihmoj sot?"
+input_ids = tokenizer(input_text, return_tensors="pt").input_ids
 
-[More Information Needed]
+# Generate text
+# Note: You might need to adjust generation parameters like max_length, num_beams, etc.
+# based on your specific needs and the model's capabilities.
+outputs = model.generate(input_ids, max_length=50)
 
-## Evaluation
+# Decode and print the generated text
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+It is important to note that since this model is a proof-of-concept and marked as deprecated, users should exercise caution and consider more recent or robust models for production applications. The example above provides a basic framework; further customization of generation parameters will likely be necessary for optimal results.
 
-### Testing Data, Factors & Metrics
+### Training Details
 
-#### Testing Data
+**Training Data**
 
-<!-- This should link to a Dataset Card if possible. -->
+The model `olsi8/gemma-3-4b-it-shqip-v1` was fine-tuned on a combination of Albanian language data. This included a collection of books and the specific dataset `olsi8/albanian-lang-gemma-format`, which is available on Hugging Face at [https://huggingface.co/datasets/olsi8/albanian-lang-gemma-format](https://huggingface.co/datasets/olsi8/albanian-lang-gemma-format). The nature of the books used is not specified in detail, but they contributed to the Albanian language corpus for training.
 
-[More Information Needed]
+**Training Procedure**
 
-#### Factors
+*Preprocessing [optional]*
+Details regarding the specific preprocessing steps applied to the training data are not extensively documented on the model card. Standard preprocessing for language models typically includes tokenization, formatting of input-output pairs, and potentially data cleaning or filtering. Given the use of the `albanian-lang-gemma-format` dataset, it is likely that the data was structured to be compatible with the Gemma model's training requirements.
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+*Training Hyperparameters*
+Specific training hyperparameters such as learning rate, batch size, number of epochs, and optimization algorithms used for fine-tuning `olsi8/gemma-3-4b-it-shqip-v1` are not detailed on its Hugging Face page. The training regime would have involved fine-tuning the pre-trained `gemma-3-4b-it` model on the aforementioned Albanian language data.
 
-[More Information Needed]
+*Speeds, Sizes, Times [optional]*
+Information about the training speed, computational resources utilized, and total training time for this specific fine-tuning effort is not provided.
 
-#### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
-[More Information Needed]
+### Evaluation
 
-### Results
+**Testing Data, Factors & Metrics**
 
-[More Information Needed]
+*Testing Data*
+The model was evaluated on the `olsi8/albanian-lang-gemma-format` dataset. Information about other specific testing datasets or benchmarks used for a broader evaluation is not readily available.
 
-#### Summary
+*Factors*
+Key factors influencing the model's performance include the size and quality of the Albanian language data it was fine-tuned on, the architecture of the base `gemma-3-4b-it` model, and the specific fine-tuning process. The model's capabilities are primarily focused on the Albanian language.
 
+*Metrics*
+The primary metric reported for this model is an accuracy of 0.77 on the `olsi8/albanian-lang-gemma-format` dataset. Other standard NLP evaluation metrics (e.g., perplexity, BLEU scores for translation-like tasks, ROUGE for summarization, etc.) are not explicitly provided for this model on its Hugging Face page.
 
+**Results**
+The reported accuracy of 0.77 on the specified dataset indicates a degree of success in learning the target task for the Albanian language. However, as a proof-of-concept model, these results should be interpreted with caution, and further evaluation on diverse benchmarks would be necessary to fully understand its performance characteristics.
 
-## Model Examination [optional]
+**Summary**
+`olsi8/gemma-3-4b-it-shqip-v1` demonstrates foundational capabilities in Albanian language processing, achieving a notable accuracy on its fine-tuning dataset. Nevertheless, its status as a proof-of-concept and deprecated model suggests that it serves more as an experimental iteration than a production-ready solution.
 
-<!-- Relevant interpretability work for the model goes here -->
+### Model Examination [optional]
+Further examination of the model's outputs, error analysis, and performance on specific linguistic phenomena in Albanian would be beneficial for a deeper understanding of its strengths and weaknesses. Such detailed examination is not provided in the current model card.
 
-[More Information Needed]
+### Environmental Impact
 
-## Environmental Impact
+Information regarding the environmental impact of fine-tuning this specific model is not provided. However, general considerations for large language models apply.
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+*   **Hardware Type:** [More Information Needed - Typically GPUs like NVIDIA A100s or H100s are used for training models of this scale, but specifics for this fine-tune are unknown]
+*   **Hours used:** [More Information Needed - Training time for fine-tuning can vary based on dataset size and hardware]
+*   **Cloud Provider:** [More Information Needed - Common providers include GCP, AWS, Azure, or private clusters]
+*   **Compute Region:** [More Information Needed]
+*   **Carbon Emitted:** [More Information Needed - Can be estimated using tools like the Machine Learning Impact calculator if hardware and usage details were available]
 
-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).
+### Technical Specifications [optional]
 
-- **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]
+**Model Architecture and Objective**
+The model utilizes the Gemma architecture, specifically the `gemma-3-4b-it` version, which has approximately 3.4 billion parameters. The objective of this fine-tuned version is causal language modeling, tailored for generating coherent and contextually relevant text in the Albanian language.
 
-## Technical Specifications [optional]
+**Compute Infrastructure**
 
-### Model Architecture and Objective
+*Hardware*
+Specific hardware used for the fine-tuning process is not detailed. Training models of this size typically requires access to high-performance GPUs.
 
-[More Information Needed]
+*Software*
+The model was developed using the Hugging Face Transformers library. Other common software includes PyTorch or TensorFlow, CUDA for GPU acceleration, and various Python libraries for data processing.
 
-### 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 [optional]
 
 **BibTeX:**
-
-[More Information Needed]
+As this is a fine-tuned model by a community user, a specific BibTeX entry for this exact model version may not exist. For the base Gemma models, refer to Google's official publications. A general citation for the model repository could be:
+
+```bibtex
+@misc{olsi8_gemma_3_4b_it_shqip_v1,
+  author = {olsi8},
+  title = {gemma-3-4b-it-shqip-v1: A fine-tuned Gemma model for Albanian},
+  year = {2024},
+  publisher = {Hugging Face},
+  journal = {Hugging Face repository},
+  howpublished = {\url{https://huggingface.co/olsi8/gemma-3-4b-it-shqip-v1}}
+}
+```
 
 **APA:**
+olsi8. (2024). *gemma-3-4b-it-shqip-v1: A fine-tuned Gemma model for Albanian*. Hugging Face. Retrieved from https://huggingface.co/olsi8/gemma-3-4b-it-shqip-v1
 
-[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]
+### Glossary [optional]
+[More Information Needed - A glossary could define terms like "fine-tuning", "causal language modeling", "Gemma architecture", etc., if deemed necessary for the audience.]
 
-[More Information Needed]
+### More Information [optional]
 
-## Model Card Authors [optional]
+This model, `olsi8/gemma-3-4b-it-shqip-v1`, should be considered a proof-of-concept for fine-tuning Gemma models for the Albanian language. It has been marked as **deprecated**. Users are advised that newer, potentially more robust models or alternative approaches may be available and should be preferred for ongoing development or production use. The model was trained on a combination of books and the `olsi8/albanian-lang-gemma-format` dataset.
 
-[More Information Needed]
+### Model Card Authors [optional]
+This model card was generated by an AI assistant based on publicly available information and user-provided details. The original model was developed and shared by the Hugging Face user 'olsi8'.
 
-## Model Card Contact
+### Model Card Contact
+For questions or issues regarding the model itself, contacting the model owner 'olsi8' through their Hugging Face profile ([https://huggingface.co/olsi8](https://huggingface.co/olsi8)) would be the most direct approach. For issues with this model card, please refer to the generating entity.
 
-[More Information Needed]