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- library_name: transformers
 
 
 
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  tags:
 
 
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  - unsloth
 
 
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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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+ base_model: unsloth/Meta-Llama-3.1-8B-bnb-4bit
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+ language:
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+ - en
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+ license: apache-2.0
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  tags:
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+ - text-generation-inference
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+ - transformers
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  - unsloth
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+ - llama
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+ - gguf
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  ---
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+ # LLAMA-3.1 8B Chat Turkish Model
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+ - **Developed by:** inetnuc
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+ - **License:** apache-2.0
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+ - **Finetuned from model:** unsloth/Meta-Llama-3.1-8B-bnb-4bit
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+ This LLAMA-3.1 model was finetuned to enhance capabilities in text generation for nuclear-related topics. The training was accelerated using [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library, achieving a 2x faster performance.
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+ ## Finetuning Process
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+ The model was finetuned using the Unsloth library, leveraging its efficient training capabilities. The process included the following steps:
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+ 1. **Data Preparation:** Loaded and preprocessed turkish-related data.
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+ 2. **Model Loading:** Utilized `unsloth/llama-3-8b-bnb-4bit` as the base model.
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+ 3. **LoRA Patching:** Applied LoRA (Low-Rank Adaptation) for efficient training.
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+ 4. **Training:** Finetuned the model using Hugging Face's TRL library with optimized hyperparameters.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ## Model Details
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ - **Base Model:** `unsloth/llama-3.1-8b-bnb-4bit`
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+ - **Language:** English (`en`)
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+ - **License:** Apache-2.0
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+ ## Author
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+ **MUSTAFA UMUT OZBEK**
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+ **https://www.linkedin.com/in/mustafaumutozbek/**
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+ **https://x.com/m_umut_ozbek**
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+ ## Usage
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+ ### Loading the Model
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+ You can load the model and tokenizer using the following code snippet:
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+ # Load the tokenizer and model
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+ tokenizer = AutoTokenizer.from_pretrained("inetnuc/TurkishLlama-3.1-8B-4bit-chat-nuclear-lora")
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+ model = AutoModelForCausalLM.from_pretrained("inetnuc/TurkishLlama-3.1-8B-4bit-chat-nuclear-lora")
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+ # Example of generating text
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+ inputs = tokenizer("Türki̇ye'de nükleer enerji̇ yatirimlari artirilmali mi, ne düşünüyorsun?", return_tensors="pt")
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+ outputs = model.generate(**inputs, max_new_tokens=128)
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+ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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