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--- |
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datasets: |
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- taesiri/TinyStories-Farsi |
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library_name: transformers |
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model_name: LLaMA-3.1-8B-Persian-Instruct |
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pipeline_tag: text-generation |
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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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--- |
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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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This model is a fine-tuned version of Llama-3.1-8B-Instruct that meta has released. By training this model on persian short stories, the new model gets to understand the relation between English and Persian in a more meaning full way. |
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- **Developed by:** Meta AI |
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- **Model type:** Language Model |
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- **License:** Apache 2.0 |
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- **Base Model:** `meta-llama/Meta-Llama-3.1-8B-Instruct` |
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### Model Sources |
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- **Repository:** [Llama-3.1-8B-Instruct on Hugging Face](https://huggingface.co./meta-llama/Meta-Llama-3.1-8B-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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import torch |
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# Specify the combined model |
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model_name = "AmirMohseni/Llama-3.1-8B-Instruct-Persian-finetuned-sft" |
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# Load the model and tokenizer |
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model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto") |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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# Ensure pad_token is set (if not already set) |
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if tokenizer.pad_token is None: |
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tokenizer.add_special_tokens({'pad_token': tokenizer.eos_token}) |
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# Check if CUDA is available, otherwise use CPU |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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model = model.to(device) |
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# Example usage |
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input_text = "چطوری میتونم به اطلاعات درباره ی سهام شرکت های آمریکایی دست پیدا کنم؟" |
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# Tokenize the input |
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inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True).to(device) |
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# Generate text |
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outputs = model.generate( |
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inputs['input_ids'], |
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attention_mask=inputs['attention_mask'], |
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max_length=512, |
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pad_token_id=tokenizer.pad_token_id |
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) |
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# Decode and print the output |
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response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(response) |
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``` |
