Note

• Please use in accordance with Llama-3.1's license terms and Acceptable Use Policy.
• Llama-3.1-8B-Instruct of 8B parameters in 4-bit bnb nf4 format
• Model Source

Introduction

Llama 3.1-8b-Instruct is part of the Llama 3.1 model family, an advanced suite of language models designed to push the boundaries of AI capabilities. With the release of the Llama 3.1 series, including the 405B flagship model, Llama models now offer state-of-the-art performance in general knowledge, steerability, mathematical reasoning, tool use, and multilingual translation. The 8B model is a streamlined, efficient version that retains many of the advanced features of its larger counterpart, making it suitable for various applications that require robust language understanding and generation.

Llama 3.1-8b-Instruct LLM

Llama 3.1-8b-Instruct is built on a standard decoder-only transformer model architecture with 8 billion parameters. It features a significantly extended context length of 128K, allowing it to handle long-form text generation and complex conversational contexts. The model has undergone rigorous training and fine-tuning processes, including supervised fine-tuning, rejection sampling, and direct preference optimization, to enhance its instruction-following capabilities and ensure high-quality, detailed responses to user queries.

Prompt Format

Llama 3.1-8b-Instruct supports conversation templates tailored for different use cases. Specify the prompt template in prompt_template variable.

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{your_system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{your_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Run Llama 3.1-8b-Instruct with an API

Running the API with Clarifai's Python SDK

You can run the Llama 3.1-8b-Instruct Model API using Clarifai’s Python SDK.

Export your PAT as an environment variable. Then, import and initialize the API Client.

Find your PAT in your security settings.

export CLARIFAI_PAT={your personal access token}

from clarifai.client.model import Model

prompt = "what's the future of AI?"
prompt_template = '''<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>'''

system_prompt= "You're the helpful assistant"
inference_params = dict(temperature=0.7, max_tokens=200, top_k = 50, top_p= 0.95, prompt_template= prompt_template, system_prompt=system_prompt)

# Model Predict
model_prediction = Model("https://clarifai.com/meta/Llama-3/models/llama-3_1-8b-instruct").predict_by_bytes(prompt.encode(), input_type="text", inference_params=inference_params)

print(model_prediction.outputs[0].data.text.raw)

You can also run Llama 3.1-8b-Instruct API using other Clarifai Client Libraries like Java, cURL, NodeJS, PHP, etc here.

Aliases: Llama 3.1-8b-Instruct, llama 3.1

Use Cases

1. Long-Form Text Summarization: Efficiently condenses extensive documents into concise summaries.
2. Multilingual Conversational Agents: Supports interactions in multiple languages with high accuracy and natural fluency.
3. Coding Assistants: Assists in code generation, debugging, and explanation, enhancing productivity for developers.
4. Educational Tools: Provides detailed explanations and tutoring across various subjects.
5. Customer Support: Enhances automated support systems by delivering accurate and context-aware responses.

Evaluation and Benchmark Results

The Llama 3.1-8B-Instruct model has undergone extensive evaluation on over 150 benchmark datasets covering a diverse range of languages and tasks. Human evaluations indicate that it performs competitively with leading models like GPT-4 and Claude 3.5 Sonnet, demonstrating strong reasoning capabilities and superior tool use.

Dataset

The training of Llama 3.1-8B-Instruct utilized over 15 trillion tokens, sourced from a carefully curated and pre-processed dataset. This dataset includes a mix of general web text, technical documents, multilingual corpora, and high-quality synthetic data generated through iterative post-training procedures.

Advantages

• Extended Context Length: Supports up to 128K tokens, enabling processing of lengthy documents and complex interactions.
• Multilingual Proficiency: Excels in translation and conversation across multiple languages.
• Enhanced Reasoning: Improved logic and problem-solving abilities..
• Quantization for Efficiency: Uses 8-bit (FP8) numerics for efficient deployment on single server nodes.

Limitations

• Computational Requirements: Despite optimizations, running the model, especially at scale, can require substantial computational resources.
• Synthetic Data Dependence: While synthetic data improves performance, there is a reliance on the quality of this data for maintaining high standards across all capabilities.
• Generalization: Although highly capable, the model may occasionally struggle with highly specialized or niche topics that fall outside the scope of its training data.
• Safety and Bias: Ongoing efforts are required to ensure the model's responses are safe and free from bias, especially as new data and applications emerge