Gradientai

Models by this creator

🔗

Llama-3-8B-Instruct-Gradient-1048k

gradientai

Total Score

598

The Llama-3-8B-Instruct-Gradient-1048k model is a large language model developed by Gradient that extends the context length of the original LLama-3 8B model from 8k to over 1048k tokens. It demonstrates that state-of-the-art LLMs can learn to operate on long context with minimal training by appropriately adjusting the Rotary Position Embedding (RoPE) theta. Gradient incorporated data from the SlimPajama dataset to train this model, which was then fine-tuned on 1.4B tokens over multiple stages with progressive increases in context length. This model builds on the Meta Llama-3-8B-Instruct base and shows improved performance on long-context tasks compared to the original LLama-3 8B model. Model inputs and outputs Inputs The model takes text-based inputs only. Outputs The model generates text and code outputs. Capabilities The Llama-3-8B-Instruct-Gradient-1048k model is capable of engaging in open-ended dialogue, answering questions, summarizing text, and generating coherent text on a wide range of topics. Its increased context length allows it to maintain coherence and consistency over longer interactions compared to the original LLama-3 8B model. What can I use it for? This model can be used for a variety of natural language processing tasks, including chatbots, assistants, content generation, and code generation. The extended context length makes it particularly well-suited for applications that require maintaining coherence over long conversations or documents, such as task-oriented dialogues, long-form content creation, and knowledge-intensive applications. Developers interested in building custom AI models or agents can contact Gradient to learn more about their end-to-end development service for large language models and AI systems. Things to try Try using the Llama-3-8B-Instruct-Gradient-1048k model for tasks that require maintaining context over long interactions, such as multi-turn dialogues, long-form document generation, or open-ended problem-solving. Experiment with different generation parameters and prompting strategies to see how the model's performance changes as the context length increases.

Read more

Updated 5/30/2024

🌿

Llama-3-8B-Instruct-262k

gradientai

Total Score

235

The Llama-3-8B-Instruct-262k model is an extension of the Meta-Llama-3-8B-Instruct model, developed by Gradient AI. This model demonstrates that state-of-the-art large language models (LLMs) can learn to operate on long contexts with minimal training by adjusting the Rotary Position Embeddings (RoPE) theta. It has a context length of over 160,000 tokens, compared to the original Llama-3 8B model's 8,000 token context. Model inputs and outputs Inputs The model accepts text input only. Outputs The model generates text outputs. Capabilities The Llama-3-8B-Instruct-262k model is capable of handling long-context tasks, such as summarization, question answering, and language generation, that require understanding and reasoning over extensive passages of text. This makes it a powerful tool for various applications that involve processing and generating long-form content. What can I use it for? The Llama-3-8B-Instruct-262k model can be used for a variety of applications that require handling long-form text, such as: Summarizing long documents or articles Answering questions based on extensive background information Generating coherent and consistent long-form content, such as reports, articles, or stories Assisting with research and analysis tasks that involve synthesizing information from multiple sources Gradient AI, the maintainer of this model, offers custom model deployment and collaboration opportunities to help businesses integrate this technology into their operations. To learn more or explore a custom model, you can contact them at [email protected]. Things to try One interesting aspect of the Llama-3-8B-Instruct-262k model is its ability to effectively process long-form text inputs. You could try providing the model with extended passages, such as journal articles, technical reports, or historical documents, and observe how it generates summaries, answers questions, or continues the narrative. This can showcase the model's capacity to maintain coherence and understanding across large amounts of input information.

Read more

Updated 5/28/2024

👨‍🏫

Llama-3-70B-Instruct-Gradient-1048k

gradientai

Total Score

96

The Llama-3-70B-Instruct-Gradient-1048k model extends the context length of the Llama-3 70B model from 8k to over 1048k tokens. It was developed by Gradient, sponsored by compute from Crusoe Energy. The model demonstrates that state-of-the-art large language models can learn to operate on long context with minimal training by appropriately adjusting the Rotary Position Embedding (RoPE) theta parameter. Gradient trained this model on 34M tokens for the final stage, and a total of around 430M tokens across all stages, which is less than 0.003% of Llama-3's original pre-training data. Similar models include the Llama-3-8B-Instruct-Gradient-1048k and Llama-3-8B-Instruct-262k models, which also extend the Llama-3 context length to 1048k and 262k respectively. Model inputs and outputs Inputs The model takes text as input. Outputs The model generates text and code. Capabilities The Llama-3-70B-Instruct-Gradient-1048k model demonstrates the ability to operate on very long contexts, making it suitable for tasks that require understanding and reasoning over large amounts of information. This could be particularly useful for applications like summarization, question answering, or tasks that involve working with lengthy documents or conversations. What can I use it for? The extended context length of this model makes it well-suited for applications that require reasoning over long-form text, such as research assistants, document summarization tools, or question answering systems for complex domains. Developers could leverage this model to build autonomous agents that can power critical operations across a business, such as customer support, task planning, or content generation. Things to try One interesting aspect of this model is the approach Gradient used to effectively train it on such long contexts. By progressively increasing the sequence length and adjusting the RoPE theta parameter, they were able to achieve strong performance with relatively little training data compared to the original Llama-3 model. Developers could experiment with this progressive training technique when fine-tuning the model for their specific use cases.

Read more

Updated 6/4/2024

👨‍🏫

Llama-3-8B-Instruct-Gradient-4194k

gradientai

Total Score

55

The Llama-3-8B-Instruct-Gradient-4194k model is an extension of the Meta Llama 3 8B instruction-tuned model, developed by Gradient. This model increases the context length from 8k to 4194k tokens, demonstrating that large language models can learn to operate on long context with minimal training by appropriately adjusting the Rotation Position Encoding (RoPE) parameter. Similar models include the Llama-3-70B-Instruct-Gradient-1048k and Llama-3-8B-Instruct-262k models, which also extend the context length of Llama 3 using progressive training and RoPE optimization techniques. Model inputs and outputs Inputs The model takes in text input only. Outputs The model generates text and code. Capabilities The Llama-3-8B-Instruct-Gradient-4194k model demonstrates strong performance on a range of benchmarks, especially for tasks that require reasoning over long contexts. By extending the context length, the model is able to maintain high performance on tasks like TriviaQA-Wiki and DROP, where longer-range understanding is important. What can I use it for? This model could be useful for a variety of applications that benefit from long-context understanding, such as question answering, task-oriented dialogues, and code generation. Developers can leverage this model as a starting point to build custom AI agents and assistants that power critical operations across their business. Those interested in working with Gradient to develop custom LLMs and AI systems can reach out at [email protected]. Things to try One interesting aspect of this model is its demonstrated ability to learn to operate on long contexts with minimal training data - just 201M tokens for this stage, and 1.6B total across all stages. This suggests the potential to adapt large language models to new domains and tasks efficiently, without requiring enormous datasets. Developers could experiment with fine-tuning this model on their own data to adapt it to specific use cases.

Read more

Updated 6/9/2024

🏅

Llama-3-70B-Instruct-Gradient-262k

gradientai

Total Score

51

The Llama-3-70B-Instruct-Gradient-262k model extends the context length of the LLama-3 70B model from 8k to over 262k tokens. Developed by Gradient AI, this model demonstrates that state-of-the-art large language models can learn to operate on long context with minimal training by appropriately adjusting the Rotary Position Embeddings (RoPE) theta parameter. Gradient trained this model on 105M tokens, which is less than 0.002% of the original pre-training data for the LLama-3 model. The model was initialized from the meta-llama/Meta-Llama-3-70B-Instruct base, and Gradient used NTK-aware interpolation and progressive training on increasing context lengths to achieve the 262k context. They also leveraged the EasyContext Blockwise RingAttention library and custom parallelism techniques to efficiently train on very long contexts. Model inputs and outputs Inputs The model takes text input only. Outputs The model generates text and code. Capabilities The Llama-3-70B-Instruct-Gradient-262k model demonstrates the ability to understand and generate coherent text over extremely long contexts, more than 30 times longer than the base LLama-3 model. This enables the model to maintain context and perform complex tasks that require access to large amounts of information. What can I use it for? The extended context length of this model makes it well-suited for applications that require reasoning over long-form documents, such as research summarization, legal analysis, or technical writing assistance. Developers looking to build custom AI agents or systems with long-term memory and coherence can leverage this model as a starting point. Things to try Given the model's ability to maintain context over long sequences, you could experiment with using it for tasks that require connecting disparate pieces of information, like question answering over multi-page reports or generating cohesive stories from brief prompts. The model's instruction-following capabilities also make it interesting to explore for interactive assistants that need to remember and build upon previous conversation context.

Read more

Updated 6/9/2024