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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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+ - language-model
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+ - fine-tuned
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+ - instruction-following
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+ - PEFT
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+ - LoRA
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+ - BitsAndBytes
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+ - Persian
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+ - Farsi
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+ - text-generation
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+ datasets:
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+ - taesiri/TinyStories-Farsi
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+ model_name: LLaMA-3.1-8B-Persian-Instruct
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+ pipeline_tag: text-generation
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  ---
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+ # LLaMA-3.1-8B-Persian-Instruct
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+ This model is a fine-tuned version of the `meta-llama/Meta-Llama-3.1-8B-Instruct` model, specifically tailored for generating and understanding Persian text. The fine-tuning was conducted using the [TinyStories-Farsi](https://huggingface.co/datasets/taesiri/TinyStories-Farsi) dataset, which includes a diverse set of short stories in Persian. The primary goal of this fine-tuning was to enhance the model's performance in instruction-following tasks within the Persian language.
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  ## Model Details
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  ### Model Description
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+ The `LLaMA-3.1-8B-Persian-Instruct` model is part of the LLaMA series known for its robust performance across various NLP tasks. This version is adapted to Persian, making it more effective for generating coherent and contextually relevant responses in this language.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ - **Developed by:** Meta AI, fine-tuned by Amir Mohseni
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+ - **Model type:** Language Model
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+ - **Language(s) (NLP):** Persian (Farsi)
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+ - **License:** Apache 2.0
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+ - **Finetuned from model:** `meta-llama/Meta-Llama-3.1-8B-Instruct`
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+ ### Model Sources
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+ - **Repository:** [LLaMA-3.1-8B-Persian-Instruct on Hugging Face](https://huggingface.co/AmirMohseni/LLaMA-3.1-8B-Persian-Instruct)
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Training Details
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  ### Training Data
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+ The model was fine-tuned using the [TinyStories-Farsi](https://huggingface.co/datasets/taesiri/TinyStories-Farsi) dataset. This dataset provided a rich and diverse linguistic context, helping the model better understand and generate text in Persian.
 
 
 
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  ### Training Procedure
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+ The fine-tuning process was conducted using the following setup:
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+ - **Epochs:** 4
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+ - **Batch Size:** 8
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+ - **Gradient Accumulation Steps:** 2
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+ - **Hardware:** NVIDIA A100 GPU
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ### Fine-Tuning Strategy
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+ To make the fine-tuning process efficient and effective, PEFT (Parameter-Efficient Fine-Tuning) techniques were employed. Specifically, the `BitsAndBytesConfig(load_in_4bit=True)` configuration was used, allowing the model to be fine-tuned in 4-bit precision. This approach significantly reduced the computational resources required while maintaining high performance, resulting in a training time of approximately 2 hours. The use of `BitsAndBytesConfig(load_in_4bit=True)` helped reduce the environmental impact by minimizing the computational resources required.
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+ ## Uses
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ### Direct Use
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+ This model is well-suited for generating text in Persian, particularly for instruction-following tasks. It can be used in applications like chatbots, customer support systems, educational tools, and more where accurate and context-aware Persian language generation is needed.
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+ ### Out-of-Scope Use
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+ The model is not intended for tasks requiring deep reasoning, complex multi-turn conversations, or contexts beyond the immediate prompt. It is also not designed for generating text in languages other than Persian.
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+ ## How to Get Started with the Model
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+ Here is how you can use this model:
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+ model_name = "AmirMohseni/LLaMA-3.1-8B-Persian-Instruct"
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+ tokenizer = AutoTokenizer.from_pretrained(model_name)
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+ model = AutoModelForCausalLM.from_pretrained(model_name)
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+ # Example usage
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+ prompt = "راه‌های تقویت حافظه چیست؟"
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+ inputs = tokenizer(prompt, return_tensors="pt")
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+ outputs = model.generate(**inputs)
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+ response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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+ print(response)
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+ ```