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Ronan Takizawa PRO

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AI & ML interests

OSS Contributor @ Google AI, Databricks, Apache. 100k+ followers online

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upvoted an article about 12 hours ago
Uncensor any LLM with abliteration
reacted to their post with 🔥 about 12 hours ago
Excited to announce 4 AWQ quantized models from #AllenAI! 🎉 Molmo-7B-D AWQ (14GB→5GB): Efficient VLM performing between GPT-4V and GPT-4o on academic benchmarks, with just 6.1% perplexity degradation. MolmoAct-7B-D AWQ (14GB→6GB): Specialized robotic manipulation model reduced by ~57%. Molmo-72B AWQ (145GB→38GB): VLM with Qwen2-72B decoder that performs competitively with GPT-4, achieving only 10.5% perplexity degradation while saving 107GB of memory. OLMo-2-32B-Instruct AWQ (64GB→17GB): LLM post-trained on Tülu 3 with 3% perplexity degradation while saving ~50GB. All VLMs only had their text models quantized. https://huggingface.co/ronantakizawa/molmo-7b-d-awq https://huggingface.co/ronantakizawa/molmoact-7b-d-awq https://huggingface.co/ronantakizawa/molmo-72b-awq https://huggingface.co/ronantakizawa/olmo2-32b-instruct-awq
posted an update 1 day ago
Excited to announce 4 AWQ quantized models from #AllenAI! 🎉 Molmo-7B-D AWQ (14GB→5GB): Efficient VLM performing between GPT-4V and GPT-4o on academic benchmarks, with just 6.1% perplexity degradation. MolmoAct-7B-D AWQ (14GB→6GB): Specialized robotic manipulation model reduced by ~57%. Molmo-72B AWQ (145GB→38GB): VLM with Qwen2-72B decoder that performs competitively with GPT-4, achieving only 10.5% perplexity degradation while saving 107GB of memory. OLMo-2-32B-Instruct AWQ (64GB→17GB): LLM post-trained on Tülu 3 with 3% perplexity degradation while saving ~50GB. All VLMs only had their text models quantized. https://huggingface.co/ronantakizawa/molmo-7b-d-awq https://huggingface.co/ronantakizawa/molmoact-7b-d-awq https://huggingface.co/ronantakizawa/molmo-72b-awq https://huggingface.co/ronantakizawa/olmo2-32b-instruct-awq
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