---
license: llama3.1
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
pipeline_tag: text-generation
library_name: transformers
tags:
- llama3.1-5B
- llama-3
- Base_Ft
- facebook
- text-generation-inference
- meta
- ollama
---
# **Llama-3.1-5B-Instruct**

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.

## Model Overview

- **Size**: 5B parameters
- **Model Architecture**: Llama-3.1 is an auto-regressive language model using an optimized transformer architecture.
- **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.

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.

## How to Use

### Requirements

- Install the latest version of **Transformers**:
  ```bash
  pip install --upgrade transformers
  ```

- Ensure you have **PyTorch** installed with support for `bfloat16`:
  ```bash
  pip install torch
  ```

### Example Code

Below is an example of how to use the **Llama-3.1-5B-Instruct** model for conversational inference:

```python
import transformers
import torch

# Define the model ID
model_id = "prithivMLmods/Llama-3.1-5B-Instruct"

# Set up the pipeline for text generation
pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",  # Use the best device available
)

# Define conversation messages
messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]

# Generate a response
outputs = pipeline(
    messages,
    max_new_tokens=256,
)

# Print the generated response
print(outputs[0]["generated_text"][-1])
```

### Model Details

- **Model Type**: Instruction-Tuned Large Language Model (LLM)
- **Training**: Trained using supervised fine-tuning and reinforcement learning with human feedback.
- **Supported Tasks**: Dialogue generation, question answering, translation, and other text-based tasks.

### Performance

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.

### Notes

- This model is optimized for safety and helpfulness, ensuring a positive user experience.
- The **torch_dtype** is set to `bfloat16` to optimize memory usage and performance.
---