Post
1551
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.
ronantakizawa/molmo-7b-d-awq
ronantakizawa/molmoact-7b-d-awq
ronantakizawa/molmo-72b-awq
ronantakizawa/olmo2-32b-instruct-awq
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.
ronantakizawa/molmo-7b-d-awq
ronantakizawa/molmoact-7b-d-awq
ronantakizawa/molmo-72b-awq
ronantakizawa/olmo2-32b-instruct-awq