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  - unsloth
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  - trl
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  - sft
 
 
 
 
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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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- [More Information Needed]
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- ### Results
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- [More Information Needed]
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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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- [More Information Needed]
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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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- [More Information Needed]
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- **APA:**
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- [More Information Needed]
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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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  - unsloth
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  - trl
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  - sft
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+ datasets:
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+ - gbharti/finance-alpaca
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+ language:
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+ - en
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+ # Load model directly
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+ tokenizer = AutoTokenizer.from_pretrained("umarigan/llama-3-openhermes-tr")
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+ model = AutoModelForCausalLM.from_pretrained("umarigan/llama-3-openhermes-tr")
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+ alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
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+
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+ ### Instruction:
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+ {}
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+
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+ ### Input:
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+ {}
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+ ### Response:
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+ {}"""
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+ inputs = tokenizer(
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+ [
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+ alpaca_prompt.format(
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+ "bir haftada 3 kilo verebileceğim 5 öneri sunabilir misin?", # Görev
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+ "", # Girdi
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+ "", # Cevap - boş bırakın!
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+ )
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+ ], return_tensors = "pt")
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+ outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
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+ tokenizer.batch_decode(outputs)
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+ Output:
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+ '<|begin_of_text|>
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+ Görev: bir haftada 3 kilo verebileceğim 5 öneri sunabilir misin?
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+ Girdi:
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+ Cevap:
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+ 1. Yeterli miktarda su içmek: Su, vücuttaki toksinlerin atılmasını ve sindirim sisteminin çalışmasını destekler. Su içmek, vücuttaki su dengesini korumaya yardımcı olur ve kilo kaybına yardımcı olabilir. Günlük su tüketiminizi 2-3 litre arasında tutun.
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+ 2. Düzenli egzersiz yapın: Egzersiz, metabolizmayı hızlandırmaya yardımcı olur ve vücuttaki yağ yakımını teşvik eder. Haftada 3-5 gün egzersiz yapın ve her gün 30-60 dakika egzersiz yapın. Egzersizler, aerobik egzersizler (yürüyüş, yüzme, bisiklet sürmek) ve ana kas egzersizleri (kollar, bacaklar, kalça) gibi çeşitli egzersiz türlerini içerebilir.
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+ 3. Azalmış kalori alımına odaklanın: Kilo verme hedefinize ulaşmak için kalori alımınızı azaltın. Bu, beslenme planınızda azalmış kalori içeren yiyecekleri seçerek ve aşırı kalori alımını önlemek için yemeklerinizi ölçerek veya ağırlaştırmaya çalışarak yapılabilir.
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+ 4. Yeterli uyku al: Uyku, metabolizmayı düzenleyen ve kilo kaybına yardımcı olan hormonların salınmasını etkileyen bir faktördür. Günlük 7-9 saat uyku almayı hedefleyin.
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+ 5. Stres yönetimi: Stres, hormonların salınmasını etkileyerek kilo alımına katkıda bulunabilir. Stresi yönetmek için egzersiz, meditasyon veya derin nefes egzersizleri gibi çeşitli yöntemleri kullanın. Bu, stresi azaltmaya ve metabolizmayı düzenleme yeteneğini geliştirmeye yardımcı olabilir.
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+ <|end_of_text|>'
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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