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  library_name: transformers
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- tags: []
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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  ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
 
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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  ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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  ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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  ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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  ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
 
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- ## Training Details
 
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- ### Training Data
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- <!-- 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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- [More Information Needed]
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  ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
 
 
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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  ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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  ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- 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).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- ## Citation [optional]
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- <!-- 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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- **BibTeX:**
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- **APA:**
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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- [More Information Needed]
 
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  library_name: transformers
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+ tags:
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+ - turkish
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+ - lora
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+ - nlp
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+ - chatbot
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+ - causal-lm
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+ - instruct
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+ - qwen2
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+ - fine-tuned
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+ - conversational
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+ model_name: Qwen2-7B-Instruct LoRA Turkish
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+ base_model: Qwen/Qwen2-7B-Instruct
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+ license: apache-2.0
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+ inference: true
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  ---
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+ # Qwen2-7B-Instruct LoRA TR
 
 
 
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+ Bu model, **Qwen/Qwen2-7B-Instruct** tabanlı olarak, Türkçe diyaloglar üzerine LoRA ve PEFT teknikleriyle fine-tune edilmiştir.
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+ Fine-tune işlemi Google Colab Pro ortamında, A100 GPU üzerinde, bitsandbytes ile quantized (8-bit) olarak gerçekleştirilmiştir.
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  ## Model Details
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+ - **Base Model:** [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct)
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+ - **Fine-tuned Model:** elifbasboga/qwen2-7b-instruct-lora-tr
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+ - **Method:** LoRA (Parameter-Efficient Fine-Tuning), PEFT
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+ - **Language(s):** Turkish
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+ - **Libraries:** transformers, peft, bitsandbytes
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+ - **License:** Apache 2.0 (base model) – lütfen kendi kullanımına göre belirle!
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+ ## Model Description
 
 
 
 
 
 
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+ This model is a Turkish conversational AI based on Qwen2-7B-Instruct, fine-tuned with LoRA adapters on custom Turkish dialog dataset.
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+ It is suitable for chatbot, assistant, and Turkish NLP tasks.
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+ ## Model Sources
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+ - [Base Model: Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct)
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+ - [This Repository](https://huggingface.co/elifbasboga/qwen2-7b-instruct-lora-tr)
 
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  ## Uses
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  ### Direct Use
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+ - Turkish conversational tasks
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+ - Chatbot and assistant applications
 
 
 
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+ ### Downstream Use
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+ - Can be further fine-tuned for domain-specific Turkish tasks.
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  ### Out-of-Scope Use
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+ - Not suitable for tasks outside Turkish language modeling.
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+ - Not recommended for critical applications without further evaluation.
 
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  ## Bias, Risks, and Limitations
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+ - May reflect biases present in the training data (Turkish conversations).
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+ - Outputs may sometimes be off-topic, incorrect, or inappropriate.
 
 
 
 
 
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+ ## Recommendations
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+ - Use responsibly and review outputs, especially in sensitive or production settings.
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+ ## How to Get Started
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+ model = AutoModelForCausalLM.from_pretrained("elifbasboga/qwen2-7b-instruct-lora-tr")
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+ tokenizer = AutoTokenizer.from_pretrained("elifbasboga/qwen2-7b-instruct-lora-tr")
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+ ```
 
 
 
 
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  ### Training Procedure
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+ - **Epochs:** 3
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+ - **Batch Size:** 1
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+ - **Learning Rate:** 2e-4
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+ - **Quantization:** 8-bit (bitsandbytes)
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+ - **Adapter:** LoRA
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+ ### Hardware
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+ - Google Colab Pro, NVIDIA A100 GPU
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+ ### Software
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+ - Python 3.11
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+ - transformers, peft, bitsandbytes, datasets
 
 
 
 
 
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  ## Evaluation
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+ - Model was evaluated qualitatively during training via loss metrics.
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+ - For best results, further testing on your own data is recommended.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Environmental Impact
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+ - **GPU:** NVIDIA A100
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+ - **Cloud Provider:** Google Colab
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ## Citation
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+ If you use this model, please cite the base model and this repository.
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+ ```bibtex
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+ @misc{elifbasboga_qwen2_7b_instruct_lora_tr_2025,
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+ title={Qwen2-7B-Instruct LoRA Turkish},
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+ author={elifbasboga},
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+ year={2025},
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+ howpublished={\url{https://huggingface.co/elifbasboga/qwen2-7b-instruct-lora-tr}},
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+ }
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+ ```
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+ ## Model Card Authors
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+ - elifbasboga
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+ ## Contact
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+ - [Hugging Face Profile](https://huggingface.co/elifbasboga)