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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":{"_id":"603d25b75f9d390ab190b777","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1617264212503-603d25b75f9d390ab190b777.jpeg","fullname":"Pedro 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177.1 TFLOPS

1427 226 416

Pedro Cuenca

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6153

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!

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210

Welcome Gemma 3: Google's all new multimodal, multilingual, long context open LLM

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Collections 4

Core ML Diffusers 🧨

Some diffusion models ported to Core ML that work with apple/ml-stable-diffusion and huggingface/swift-coreml-diffusers.

• 

  apple/coreml-stable-diffusion-mixed-bit-palettization

  Updated Jul 29, 2023 • 208 • 22
• 

  apple/coreml-stable-diffusion-xl-base

  Text-to-Image • Updated Jul 27, 2023 • 42 • 67
• 

  apple/coreml-stable-diffusion-2-1-base

  Text-to-Image • Updated May 1, 2023 • 177 • 49
• 

  pcuenq/coreml-stable-diffusion-2-1-base

  Text-to-Image • Updated Dec 29, 2022 • 102 • 3

Core ML Text Generation

[WIP] On-device LLMs https://huggingface.co/blog/swift-coreml-llm

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  90

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  Transformers to Core ML

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• 

  coreml-projects/Llama-2-7b-chat-coreml

  Text Generation • Updated Jul 18, 2023 • 3.7k • 137
• 

  tiiuae/falcon-7b-instruct

  Text Generation • Updated Oct 12, 2024 • 164k • • 948

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spaces 10

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5

Matryoshka

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Generate images from text descriptions

Paused

Cpu

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Paused

Gguf It

🌖

Runtime error

Quarto Template

🌖

Runtime error

65

ControlNet Uncanny Faces

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Sleeping

2

Persistent Chatroom

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Enter chat and send messages

Expand 10 spaces

models 108

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pcuenq/gated-test

Text Generation • Updated 7 days ago

pcuenq/DeepSeek-R1-Distill-Qwen-32B-Q2-6

Text Generation • Updated Feb 5 • 126 • 1

pcuenq/jh_dreambooth_1000

Text-to-Image • Updated Feb 5 • 74 • 1

pcuenq/gemma-2-2b-it-Q4_K_M

Text Generation • Updated Feb 5 • 9

pcuenq/gpt2-finetuned-cnn-summarization-v2-tokenizer

Text Generation • Updated Jan 31 • 16

pcuenq/smol-lfs

Updated Jan 29

pcuenq/vit_large_patch14_dinov2.lvd142m

Image Feature Extraction • Updated Jan 21 • 9

pcuenq/vit_base_patch32_clip_448.laion2b_ft_in12k_in1k

Image Classification • Updated Jan 20 • 77

pcuenq/tw1

Image Classification • Updated Jan 20 • 20

pcuenq/timm-wrapper-food-lora

Updated Jan 20

Expand 108 models

datasets 17

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pcuenq/diffusers-videos

Viewer • Updated Jan 20 • 4 • 28

pcuenq/coco-semantic-segmentation

Viewer • Updated May 9, 2024 • 5k • 188

pcuenq/misc-assets

Updated May 8, 2024 • 28

pcuenq/media

Updated Apr 30, 2024

pcuenq/tests

Viewer • Updated Jan 6, 2024 • 27 • 809

pcuenq/amused_mps

Updated Jan 4, 2024 • 23

pcuenq/gists

Updated Nov 13, 2023 • 25 • 6

pcuenq/recipe-images

Viewer • Updated Jul 27, 2023 • 8 • 40

pcuenq/face_synthetics_spiga

Viewer • Updated Mar 20, 2023 • 100k • 1.79k • 13

pcuenq/face_synthetics_spiga_smol

Viewer • Updated Mar 19, 2023 • 100 • 103

Expand 17 datasets