Winglian
Models by this creator
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Llama-3-8b-64k-PoSE
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Llama-3-8b-64k-PoSE is a large language model (LLM) developed by winglian that extends the context length of the Llama 3 8B model from 8k to 64k tokens using Packed Sparse Attention (PoSE). The model was trained on a subset of the RedPajama v1 dataset with text between 6k-8k tokens, and further fine-tuned with a rank stabilized LoRA. Compared to the base Llama 3 8B model, this extended context version can handle longer input sequences. Similar models include the Meta-Llama-3-8B and Meta-Llama-3-70B models, which are also part of the Llama 3 family developed by Meta. These models come in 8B and 70B parameter sizes and have both pre-trained and instruction-tuned versions. Model inputs and outputs Inputs The model takes in text input only. Outputs The model generates text and code. Capabilities Llama-3-8b-64k-PoSE can handle longer input sequences than the base Llama 3 8B model due to its extended 64k token context length. This makes it well-suited for tasks that require processing of long-form text, such as summarization, question answering on lengthy passages, or text generation with large context windows. What can I use it for? The extended context capabilities of Llama-3-8b-64k-PoSE make it a good choice for applications that need to work with long-form text, such as academic writing assistance, long-form journalism, or analysis of lengthy documents. Developers could fine-tune the model further for specific use cases to leverage its ability to maintain coherence and context over longer spans of text. Things to try One interesting aspect of this model is the use of Packed Sparse Attention (PoSE) to extend the context length. Developers could experiment with different PoSE hyperparameters or explore other techniques for increasing the context window of large language models. Additionally, the model's performance on tasks that require long-range understanding, such as multi-document summarization or long-form question answering, would be an interesting area to investigate further.
Updated 5/28/2024
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llama-3-8b-256k-PoSE
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The llama-3-8b-256k-PoSE model is an extension of the Llama 3 family of large language models (LLMs) developed and released by Meta. It uses the PoSE technique to extend the model's context length from 8k to 256k tokens, enabling it to handle longer sequences of text. This model was built upon the 64k context Llama 3 model with additional pretraining data from the SlimPajama dataset. The Llama 3 models come in two sizes, 8B and 70B parameters, with both pretrained and instruction-tuned variants. These models are optimized for dialogue use cases and outperform many open-source chat models on common benchmarks. Meta has also taken great care to optimize the helpfulness and safety of these models during development. Model inputs and outputs Inputs The model accepts text input only. Outputs The model generates text and code only. Capabilities The llama-3-8b-256k-PoSE model can handle longer sequences of text due to its extended 256k context length, which is an improvement over the standard 8k context of the Llama 3 models. This can be useful for tasks that require processing of longer-form content, such as summarization, question answering, or content generation. What can I use it for? The llama-3-8b-256k-PoSE model can be used for a variety of natural language generation tasks, such as text summarization, content creation, and question answering. Its extended context length makes it well-suited for handling longer-form inputs, which could be beneficial for applications like document processing, research assistance, or creative writing. Things to try One interesting aspect of the llama-3-8b-256k-PoSE model is its ability to handle longer sequences of text. You could try using the model for tasks that involve processing lengthy documents or generating coherent long-form content. Additionally, you could explore the model's performance on benchmarks that require understanding and reasoning over extended contexts, such as open-domain question answering or multi-document summarization.
Updated 9/6/2024