NexaAIDev/omnivision-968M
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Syahmi Azhar
prsyahmi
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You are all happy 😊 that
@meta-llama
released Llama 3.
Then you are sad 😔 that it only has a context length of 8k.
Then you are happy 😄 that you can just scale llama-3 PoSE to 96k without training, only needing to modify max_position_embeddings and rope_theta.
But then you are sad 😢 it only improves the model's long-context retrieval performance (i.e., finding needles) while hardly improving its long-context utilization capability (doing QA and summarization).
But then you are happy 😁 that the
@GradientsTechnologies community has released the long-context Llama-3-8B-Instruct-262K with long context (262k-1M+).
Now we have another paper "Extending Llama-3's Context Ten-Fold Overnight" 📜.
The context length of Llama-3-8B-Instruct is extended from 8K to 80K using QLoRA fine-tuning⚙️.
The training cycle is highly efficient, taking "only" 😂 8 hours on a single 8xA800 (80G) GPU machine.
The model also preserves its original capability over short contexts. ✁
The dramatic context extension is mainly attributed to merely 3.5K synthetic training samples generated by GPT-4.📊
The paper suggests that the context length could be extended far beyond 80K with more computation resources (😅 GPU-poor).
The team plans to publicly release all resources, including data, model, data generation pipeline, and training code, to facilitate future research from the ❤️ community.
Paper: https://arxiv.org/abs/2404.19553
This is where we are... until next time... 🌟
Extending Llama-3's Context Ten-Fold Overnight (2404.19553)
Then you are sad 😔 that it only has a context length of 8k.
Then you are happy 😄 that you can just scale llama-3 PoSE to 96k without training, only needing to modify max_position_embeddings and rope_theta.
But then you are sad 😢 it only improves the model's long-context retrieval performance (i.e., finding needles) while hardly improving its long-context utilization capability (doing QA and summarization).
But then you are happy 😁 that the
@GradientsTechnologies community has released the long-context Llama-3-8B-Instruct-262K with long context (262k-1M+).
Now we have another paper "Extending Llama-3's Context Ten-Fold Overnight" 📜.
The context length of Llama-3-8B-Instruct is extended from 8K to 80K using QLoRA fine-tuning⚙️.
The training cycle is highly efficient, taking "only" 😂 8 hours on a single 8xA800 (80G) GPU machine.
The model also preserves its original capability over short contexts. ✁
The dramatic context extension is mainly attributed to merely 3.5K synthetic training samples generated by GPT-4.📊
The paper suggests that the context length could be extended far beyond 80K with more computation resources (😅 GPU-poor).
The team plans to publicly release all resources, including data, model, data generation pipeline, and training code, to facilitate future research from the ❤️ community.
Paper: https://arxiv.org/abs/2404.19553
This is where we are... until next time... 🌟
Extending Llama-3's Context Ten-Fold Overnight (2404.19553)
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readme: Fix chat template
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#2 opened 8 months ago
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prsyahmi
prsyahmi
Chat template
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#1 opened 8 months ago
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prsyahmi
prsyahmi
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