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  - text-generation-inference
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  - meta
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  - ollama
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
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  - text-generation-inference
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  - meta
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  - ollama
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+ ---
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+ # **Llama-3.1-5B-Instruct**
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+
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+ Llama-3.1 is a collection of multilingual large language models (LLMs) that includes pretrained and instruction-tuned generative models in various sizes. The **Llama-3.1-5B-Instruct** model is part of the series optimized for multilingual dialogue use cases, offering powerful conversational abilities and outperforming many open-source and closed chat models on key industry benchmarks.
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+
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+ ## Model Overview
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+
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+ - **Size**: 5B parameters
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+ - **Model Architecture**: Llama-3.1 is an auto-regressive language model using an optimized transformer architecture.
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+ - **Training**: The model is fine-tuned using Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF) to align with human preferences, ensuring helpfulness, safety, and natural conversations.
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+
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+ The **Llama-3.1-5B-Instruct** model is optimized for multilingual text generation and excels in a variety of dialog-based use cases. It is designed to handle a wide array of tasks, including question answering, translation, and instruction following.
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+
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+ ## How to Use
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+
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+ ### Requirements
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+
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+ - Install the latest version of **Transformers**:
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+ ```bash
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+ pip install --upgrade transformers
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+ ```
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+
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+ - Ensure you have **PyTorch** installed with support for `bfloat16`:
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+ ```bash
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+ pip install torch
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+ ```
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+
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+ ### Example Code
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+
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+ Below is an example of how to use the **Llama-3.1-5B-Instruct** model for conversational inference:
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+
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+ ```python
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+ import transformers
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+ import torch
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+
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+ # Define the model ID
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+ model_id = "prithivMLmods/Llama-3.1-5B-Instruct"
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+
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+ # Set up the pipeline for text generation
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+ pipeline = transformers.pipeline(
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+ "text-generation",
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+ model=model_id,
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+ model_kwargs={"torch_dtype": torch.bfloat16},
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+ device_map="auto", # Use the best device available
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+ )
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+
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+ # Define conversation messages
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+ messages = [
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+ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
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+ {"role": "user", "content": "Who are you?"},
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+ ]
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+
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+ # Generate a response
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+ outputs = pipeline(
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+ messages,
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+ max_new_tokens=256,
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+ )
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+
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+ # Print the generated response
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+ print(outputs[0]["generated_text"][-1])
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+ ```
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+
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+ ### Model Details
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+
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+ - **Model Type**: Instruction-Tuned Large Language Model (LLM)
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+ - **Training**: Trained using supervised fine-tuning and reinforcement learning with human feedback.
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+ - **Supported Tasks**: Dialogue generation, question answering, translation, and other text-based tasks.
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+
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+ ### Performance
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+
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+ The **Llama-3.1-5B-Instruct** model outperforms many existing models on several benchmarks, making it a reliable choice for conversational AI tasks in multilingual environments.
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+
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+ ### Notes
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+
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+ - This model is optimized for safety and helpfulness, ensuring a positive user experience.
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+ - The **torch_dtype** is set to `bfloat16` to optimize memory usage and performance.
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  ---