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Their quality, according to benchmarks, is similar to OLMo models of comparable size, but they required half the pre-training tokens because they use layer-wise scaling, where the number of attention heads increases in deeper layers.","raw":"Apple recently released a set of efficient LLMs in sizes varying between 270M and 3B parameters. Their quality, according to benchmarks, is similar to OLMo models of comparable size, but they required half the pre-training tokens because they use layer-wise scaling, where the number of attention heads increases in deeper layers."},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"I converted these models to Core ML, for use on Apple Silicon, using this script: ","raw":"I converted these models to Core ML, for use on Apple Silicon, using this script: "},{"type":"link","href":"https://gist.github.com/pcuenca/23cd08443460bc90854e2a6f0f575084","raw":"https://gist.github.com/pcuenca/23cd08443460bc90854e2a6f0f575084"},{"type":"text","value":". The converted models were uploaded to this community in the Hub for anyone that wants to integrate inside their apps: ","raw":". The converted models were uploaded to this community in the Hub for anyone that wants to integrate inside their apps: "},{"type":"resource","resource":{"type":"collection","id":"corenet-community/openelm-core-ml-6630c6b19268a5d878cfd194"},"url":"https://huggingface.co/collections/corenet-community/openelm-core-ml-6630c6b19268a5d878cfd194","raw":"https://huggingface.co/collections/corenet-community/openelm-core-ml-6630c6b19268a5d878cfd194"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"The conversion was done with the following parameters:","raw":"The conversion was done with the following parameters:"},{"type":"new_line","raw":"\n"},{"type":"text","value":"- Precision: float32.","raw":"- Precision: float32."},{"type":"new_line","raw":"\n"},{"type":"text","value":"- Sequence length: fixed to 128.","raw":"- Sequence length: fixed to 128."},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"With swift-transformers (","raw":"With swift-transformers ("},{"type":"link","href":"https://github.com/huggingface/swift-transformers","raw":"https://github.com/huggingface/swift-transformers"},{"type":"text","value":"), I'm getting about 56 tok/s with the 270M on my M1 Max, and 6.5 with the largest 3B model. These speeds could be improved by converting to ","raw":"), I'm getting about 56 tok/s with the 270M on my M1 Max, and 6.5 with the largest 3B model. These speeds could be improved by converting to "},{"type":"inline_code","code":"float16","raw":"`float16`"},{"type":"text","value":". However, there's some precision loss somewhere and generation doesn't work in ","raw":". However, there's some precision loss somewhere and generation doesn't work in "},{"type":"inline_code","code":"float16","raw":"`float16`"},{"type":"text","value":" mode yet. I'm looking into this and will keep you posted! Or take a look at this issue if you'd like to help: ","raw":" mode yet. I'm looking into this and will keep you posted! Or take a look at this issue if you'd like to help: "},{"type":"link","href":"https://github.com/huggingface/swift-transformers/issues/95","raw":"https://github.com/huggingface/swift-transformers/issues/95"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"I'm also looking at optimizing inference using an experimental kv cache in swift-transformers. It's a bit tricky because the layers have varying number of attention heads, but I'm curious to see how much this feature can accelerate performance in this model family :)","raw":"I'm also looking at optimizing inference using an experimental kv cache in swift-transformers. It's a bit tricky because the layers have varying number of attention heads, but I'm curious to see how much this feature can accelerate performance in this model family :)"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"Regarding the instruct fine-tuned models, I don't know the chat template that was used. The models use the Llama 2 tokenizer, but the Llama 2 chat template, or the default Alignment Handbook one that was used to train, are not recognized. Any ideas on this welcome!","raw":"Regarding the instruct fine-tuned models, I don't know the chat template that was used. The models use the Llama 2 tokenizer, but the Llama 2 chat template, or the default Alignment Handbook one that was used to train, are not recognized. Any ideas on this welcome!"}],"rawContent":"OpenELM in Core ML\n\nApple recently released a set of efficient LLMs in sizes varying between 270M and 3B parameters. Their quality, according to benchmarks, is similar to OLMo models of comparable size, but they required half the pre-training tokens because they use layer-wise scaling, where the number of attention heads increases in deeper layers.\n\nI converted these models to Core ML, for use on Apple Silicon, using this script: https://gist.github.com/pcuenca/23cd08443460bc90854e2a6f0f575084. The converted models were uploaded to this community in the Hub for anyone that wants to integrate inside their apps: https://huggingface.co/collections/corenet-community/openelm-core-ml-6630c6b19268a5d878cfd194\n\nThe conversion was done with the following parameters:\n- Precision: float32.\n- Sequence length: fixed to 128.\n\nWith swift-transformers (https://github.com/huggingface/swift-transformers), I'm getting about 56 tok/s with the 270M on my M1 Max, and 6.5 with the largest 3B model. These speeds could be improved by converting to `float16`. However, there's some precision loss somewhere and generation doesn't work in `float16` mode yet. I'm looking into this and will keep you posted! Or take a look at this issue if you'd like to help: https://github.com/huggingface/swift-transformers/issues/95\n\nI'm also looking at optimizing inference using an experimental kv cache in swift-transformers. It's a bit tricky because the layers have varying number of attention heads, but I'm curious to see how much this feature can accelerate performance in this model family :)\n\nRegarding the instruct fine-tuned models, I don't know the chat template that was used. The models use the Llama 2 tokenizer, but the Llama 2 chat template, or the default Alignment Handbook one that was used to train, are not recognized. Any ideas on this welcome!","author":{"avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1617264212503-603d25b75f9d390ab190b777.jpeg","fullname":"Pedro 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Pedro Cuenca

pcuenq

pcuenca

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Powerful ASR + diarization + speculative decoding with Hugging Face Inference Endpoints

5 days ago

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OpenELM in Core ML

Apple recently released a set of efficient LLMs in sizes varying between 270M and 3B parameters. Their quality, according to benchmarks, is similar to OLMo models of comparable size, but they required half the pre-training tokens because they use layer-wise scaling, where the number of attention heads increases in deeper layers.

I converted these models to Core ML, for use on Apple Silicon, using this script: https://gist.github.com/pcuenca/23cd08443460bc90854e2a6f0f575084. The converted models were uploaded to this community in the Hub for anyone that wants to integrate inside their apps: corenet-community/openelm-core-ml-6630c6b19268a5d878cfd194

The conversion was done with the following parameters:
- Precision: float32.
- Sequence length: fixed to 128.

With swift-transformers (https://github.com/huggingface/swift-transformers), I'm getting about 56 tok/s with the 270M on my M1 Max, and 6.5 with the largest 3B model. These speeds could be improved by converting to float16. However, there's some precision loss somewhere and generation doesn't work in float16 mode yet. I'm looking into this and will keep you posted! Or take a look at this issue if you'd like to help: https://github.com/huggingface/swift-transformers/issues/95

I'm also looking at optimizing inference using an experimental kv cache in swift-transformers. It's a bit tricky because the layers have varying number of attention heads, but I'm curious to see how much this feature can accelerate performance in this model family :)

Regarding the instruct fine-tuned models, I don't know the chat template that was used. The models use the Llama 2 tokenizer, but the Llama 2 chat template, or the default Alignment Handbook one that was used to train, are not recognized. Any ideas on this welcome!