Llama-3 70B Instruct Gradient 1048k

A high-performance language model developed by the Gradient AI team, supporting long text generation and dialogue.

CommonProductProgrammingLanguage ModelLong Text Processing

Llama-3 70B Instruct Gradient 1048k is an advanced language model developed by the Gradient AI team. By extending the context length to over 1048K, it demonstrates that SOTA (State of the Art) language models can learn to process long text after appropriate adjustments. The model employs NTK-aware interpolation and RingAttention technology, along with the EasyContext Blockwise RingAttention library, to efficiently train on high-performance computing clusters. It has widespread application potential in commercial and research applications, especially in scenarios requiring long text processing and generation.

Supports long text generation with context length extended to 1048K.
Based on the large language model family Meta Llama 3
optimized for conversation use cases.
Trained using NTK-aware interpolation and RingAttention technology.
Trained on Crusoe Energy's high-performance L40S cluster to support long text processing.
Long text generated is refined through data enhancement and conversation datasets.
The model undergoes detailed adjustments for security and performance to reduce false rejections and enhance user experience.

Appropriate for commercial intelligence assistants that require handling large volumes of text and complex conversations.
Suitable for researchers in the field of natural language processing for experiment and model training.
For developers
it can be used to create customized AI models or agents to support critical business operations.

As a chatbot
providing customer service support.
In content creation
generating creative copy and stories.
In the education field

Step 1: Visit the Llama-3 70B Instruct Gradient 1048k page within the Hugging Face model library.
Step 2: Choose to use the transformers library or the original llama3 code library for model loading based on your needs.
Step 3: Configure model parameters and load the model using the provided code snippets.
Step 4: Prepare input text or dialogue messages and process them using the model's tokenizer.
Step 5: Set the parameters for generated text

Visit

Llama-3 70B Instruct Gradient 1048k Visit Over Time

Monthly Visits

19075321

Bounce Rate

45.07%

Page per Visit

5.5

Visit Duration

00:05:32

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Llama-3 70B Instruct Gradient 1048k

Llama-3 70B Instruct Gradient 1048k Visit Over Time

Llama-3 70B Instruct Gradient 1048k Visit Trend

Llama-3 70B Instruct Gradient 1048k Visit Geography

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