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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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Ink","type":"user","_id":"636dd4bbffbe479c979ca050","isPro":false,"avatarUrl":"/avatars/4f5c80484148ab565cfe2a693f56920e.svg"},{"user":"cruelpleasure","fullname":"Rolf Hörner","type":"user","_id":"6317ee3e212fce5a3cdb47d9","isPro":false,"avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1662512684945-noauth.png"},{"user":"gth0259","fullname":"guoth","type":"user","_id":"65be61b5358734fd09fb5925","isPro":false,"avatarUrl":"/avatars/45f6dce8db12452fa39dd8902c321a32.svg"},{"user":"lgabs","fullname":"Luan Fernandes","type":"user","_id":"5ec98f68968f6028e0559f7a","isPro":false,"avatarUrl":"/avatars/2bcae1ddd48f0e38104ad99ddb22f707.svg"}],"isWatching":false,"hardwareItems":[{"sku":["GPU","NVIDIA","RTX 6000 Ada"],"mem":48,"num":1},{"sku":["GPU","NVIDIA","RTX 3090"],"mem":24,"num":1},{"sku":["GPU","NVIDIA","RTX 3090 Ti"],"mem":24,"num":1},{"sku":["Apple Silicon","-","Apple M1 Max"],"mem":64,"num":1}],"acceptLanguages":["en","*"]}">

177.1 TFLOPS

1147 145 324

Pedro Cuenca

pcuenq

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Recent Activity

updated a model about 14 hours ago

meta-llama/Llama-3.2-90B-Vision-Instruct

New activity about 14 hours ago

meta-llama/Llama-3.2-90B-Vision-Instruct:Update chat template

New activity about 20 hours ago

HuggingFaceTB/SmolLM2-1.7B-Instruct:Fix typo

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Articles

SmolVLM - small yet mighty Vision Language Model

9 days ago

• 113

Faster Text Generation with Self-Speculative Decoding

15 days ago

• 39

Fixing Gradient Accumulation

Oct 16

• 43

Llama can now see and run on your device - welcome Llama 3.2

Sep 25

• 171

FineVideo: behind the scenes

Sep 23

• 24

Fine-tuning LLMs to 1.58bit: extreme quantization made easy

Sep 18

• 205

Google releases Gemma 2 2B, ShieldGemma and Gemma Scope

Jul 31

• 59

Llama 3.1 - 405B, 70B & 8B with multilinguality and long context

Jul 23

• 220

WWDC 24: Running Mistral 7B with Core ML

Jul 22

• 56

Welcome Gemma 2 - Google's new open LLM

Jun 27

• 124

PaliGemma – Google's Cutting-Edge Open Vision Language Model

May 14

• 215

License to Call: Introducing Transformers Agents 2.0

May 13

• 118

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

May 1

• 69

Welcome Llama 3 - Meta's new open LLM

Apr 18

• 279

CodeGemma - an official Google release for code LLMs

Apr 9

• 99

Welcome Gemma - Google's new open LLM

Feb 21

• 19

Mixture of Experts Explained

Dec 11, 2023

• 201

Welcome Mixtral - a SOTA Mixture of Experts on Hugging Face

Dec 11, 2023

• 11

SDXL in 4 steps with Latent Consistency LoRAs

Nov 9, 2023

• 11

Accelerating Stable Diffusion XL Inference with JAX on Cloud TPU v5e

Oct 3, 2023

• 5

Inference for PROs

Sep 22, 2023

• 51

Introducing Würstchen: Fast Diffusion for Image Generation

Sep 13, 2023

• 12

Spread Your Wings: Falcon 180B is here

Sep 6, 2023

• 4

Code Llama: Llama 2 learns to code

Aug 25, 2023

• 8

Releasing Swift Transformers: Run On-Device LLMs in Apple Devices

Aug 8, 2023

• 24

Stable Diffusion XL on Mac with Advanced Core ML Quantization

Jul 27, 2023

• 4

Happy 1st anniversary 🤗 Diffusers!

Jul 20, 2023

• 1

Llama 2 is here - get it on Hugging Face

Jul 18, 2023

• 22

Faster Stable Diffusion with Core ML on iPhone, iPad, and Mac

Jun 15, 2023

• 4

The Falcon has landed in the Hugging Face ecosystem

Jun 5, 2023

• 9

Train your ControlNet with diffusers

Mar 24, 2023

• 18

Swift Diffusers: Fast Stable Diffusion for Mac

Feb 24, 2023

• 4

Using LoRA for Efficient Stable Diffusion Fine-Tuning

Jan 26, 2023

• 39

Using Stable Diffusion with Core ML on Apple Silicon

Dec 1, 2022

• 4

Hugging Face Machine Learning Demos on arXiv

Nov 17, 2022

Training Stable Diffusion with Dreambooth using 🧨 Diffusers

Nov 7, 2022

• 16

Stable Diffusion in JAX/Flax 🚀

Oct 13, 2022

• 2

Stable Diffusion with 🧨 Diffusers

Aug 22, 2022

• 37

Organizations

Posts 1

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Post

4454

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!

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 • 36 • 19
• 

  apple/coreml-stable-diffusion-xl-base

  Text-to-Image • Updated Jul 27, 2023 • 47 • 67
• 

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

  Text-to-Image • Updated May 1, 2023 • 36 • 44
• 

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

  Text-to-Image • Updated Dec 29, 2022 • 28 • 3

Core ML Text Generation

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

• Runtime error

  83

  ⚡

  Transformers to Core ML

• 

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

  Text Generation • Updated Jul 18, 2023 • 509 • 134
• 

  tiiuae/falcon-7b-instruct

  Text Generation • Updated Oct 12 • 202k • • 925

Expand 4 collections

spaces 10

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Running on Zero

5

🪆

Matryoshka

Paused

👁

Cpu

Paused

🌖

Gguf It

Runtime error

🌖

Quarto Template

Runtime error

65

😻

ControlNet Uncanny Faces

Sleeping

2

🐨

Persistent Chatroom

Expand 10 spaces

models 91

Sort:  Recently updated

pcuenq/misc-examples

Updated 29 days ago

pcuenq/TinyLlama-1.1B-Chat-v1.0-Q4-mlx

Updated Oct 17 • 5

pcuenq/TinyLlama-1.1B-Chat-v1.0-Q8-mlx

Updated Oct 17 • 4

pcuenq/TinyLlama-1.1B-Chat-v1.0-mlx

Updated Oct 17 • 4

pcuenq/ministral-tokenizer-wip-untested

Updated Oct 17

pcuenq/Llama-3.2-1B-Q4_K_M-GGUF

Text Generation • Updated Oct 16 • 6

pcuenq/t

Updated Oct 15

pcuenq/Llama-3.1-8B-Q4_K_M-GGUF

Text Generation • Updated Oct 15 • 6

pcuenq/Llama-3.2-1B-Instruct-Q8_0-GGUF

Text Generation • Updated Sep 30 • 5

pcuenq/Qwen2.5-0.5B-Instruct-with-new-merges-serialization-Q8_0-GGUF

Text Generation • Updated Sep 30 • 20 • 3

Expand 91 models

datasets 16

Sort:  Recently updated

pcuenq/coco-semantic-segmentation

Preview • Updated May 9 • 180

pcuenq/misc-assets

Updated May 8 • 25

pcuenq/media

Updated Apr 30 • 4

pcuenq/tests

Viewer • Updated Jan 6 • 27 • 256

pcuenq/amused_mps

Updated Jan 4 • 13

pcuenq/gists

Updated Nov 13, 2023 • 17 • 6

pcuenq/recipe-images

Viewer • Updated Jul 27, 2023 • 8 • 29

pcuenq/face_synthetics_spiga

Viewer • Updated Mar 20, 2023 • 100k • 2.41k • 12

pcuenq/face_synthetics_spiga_smol

Viewer • Updated Mar 19, 2023 • 100 • 51

pcuenq/face_synthetics

Viewer • Updated Mar 13, 2023 • 100k • 991 • 4

Expand 16 datasets