mistralai/Devstral-Small-2505
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The era of local Computer Use AI Agents is here.
Meet UI-TARS-1.5-7B-6bit, now running natively on Apple Silicon via MLX.
The video is of UI-TARS-1.5-7B-6bit completing the prompt "draw a line from the red circle to the green circle, then open reddit in a new tab" running entirely on MacBook. The video is just a replay, during actual usage it took between 15s to 50s per turn with 720p screenshots (on avg its ~30s per turn), this was also with many apps open so it had to fight for memory at times.
Built using c/ua : https://github.com/trycua/cua
Join us making them here: https://discord.gg/4fuebBsAUj
Kudos to the MLX community here on huggingface :
mlx-community
Meet UI-TARS-1.5-7B-6bit, now running natively on Apple Silicon via MLX.
The video is of UI-TARS-1.5-7B-6bit completing the prompt "draw a line from the red circle to the green circle, then open reddit in a new tab" running entirely on MacBook. The video is just a replay, during actual usage it took between 15s to 50s per turn with 720p screenshots (on avg its ~30s per turn), this was also with many apps open so it had to fight for memory at times.
Built using c/ua : https://github.com/trycua/cua
Join us making them here: https://discord.gg/4fuebBsAUj
Kudos to the MLX community here on huggingface :
mlx-community
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Lumier – Run macOS & Linux VMs in a Docker
Lumier is an open-source tool for running macOS virtual machines in Docker containers on Apple Silicon Macs.
When building virtualized environments for AI agents, we needed a reliable way to package and distribute macOS VMs. Inspired by projects like dockur/macos that made macOS running in Docker possible, we wanted to create something similar but optimized for Apple Silicon.
The existing solutions either didn't support M-series chips or relied on KVM/Intel emulation, which was slow and cumbersome. We realized we could leverage Apple's Virtualization Framework to create a much better experience.
Lumier takes a different approach: It uses Docker as a delivery mechanism (not for isolation) and connects to a lightweight virtualization service (lume) running on your Mac.
Lumier is 100% open-source under MIT license and part of C/ua.
Github : https://github.com/trycua/cua/tree/main/libs/lumier
Join the discussion here : https://discord.gg/fqrYJvNr4a
Lumier is an open-source tool for running macOS virtual machines in Docker containers on Apple Silicon Macs.
When building virtualized environments for AI agents, we needed a reliable way to package and distribute macOS VMs. Inspired by projects like dockur/macos that made macOS running in Docker possible, we wanted to create something similar but optimized for Apple Silicon.
The existing solutions either didn't support M-series chips or relied on KVM/Intel emulation, which was slow and cumbersome. We realized we could leverage Apple's Virtualization Framework to create a much better experience.
Lumier takes a different approach: It uses Docker as a delivery mechanism (not for isolation) and connects to a lightweight virtualization service (lume) running on your Mac.
Lumier is 100% open-source under MIT license and part of C/ua.
Github : https://github.com/trycua/cua/tree/main/libs/lumier
Join the discussion here : https://discord.gg/fqrYJvNr4a
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Introducing bitnet-r1-llama-8b and bitnet-r1-qwen-32b preview! These models are the first successful sub 1-billion-token finetune to BitNet architecture. We discovered that by adding an aditional input RMSNorm to each linear, you can finetune directly to BitNet with fast convergence to original model performance!
We are working on a pull request to use this extra RMS for any model.
To test these models now, install this fork of transformers:
Then load the models and test:
bitnet-r1-llama-8b and bitnet-r1-llama-32b were trained on ~ 300M and 200M tokens of the open-thoughts/OpenThoughts-114k dataset respectively, and were still significantly improving at the end of training. This preview simply demonstrates that the concept works, for future training runs we will leave the lm_head unquantized and align the last hidden state with the original model.
Huge thanks to the team that made this possible:
Gavin Childress, Aaron Herbst, Gavin Jones, Jasdeep Singh, Eli Vang, and Keagan Weinstock from the MSOE AI Club.
We are working on a pull request to use this extra RMS for any model.
To test these models now, install this fork of transformers:
pip install git+https://github.com/Codys12/transformers.gitThen load the models and test:
from transformers import (AutoModelForCausalLM, AutoTokenizer)
model_id = "codys12/bitnet-r1-qwen-32b"
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="cuda",
)
tokenizer = AutoTokenizer.from_pretrained(model_id, padding_side="left")bitnet-r1-llama-8b and bitnet-r1-llama-32b were trained on ~ 300M and 200M tokens of the open-thoughts/OpenThoughts-114k dataset respectively, and were still significantly improving at the end of training. This preview simply demonstrates that the concept works, for future training runs we will leave the lm_head unquantized and align the last hidden state with the original model.
Huge thanks to the team that made this possible:
Gavin Childress, Aaron Herbst, Gavin Jones, Jasdeep Singh, Eli Vang, and Keagan Weinstock from the MSOE AI Club.
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Matrix Game 🎮 an interactive foundation model for controllable game world generation, released by Skywork AI.
Skywork/Matrix-Game
✨ 17B with MIT licensed
✨ Diffusion-based image-to-world video generation via keyboard & mouse input
✨ GameWorld Score benchmark for Minecraft world models
✨ Massive Matrix Game Dataset with fine-grained action labels
Skywork/Matrix-Game
✨ 17B with MIT licensed
✨ Diffusion-based image-to-world video generation via keyboard & mouse input
✨ GameWorld Score benchmark for Minecraft world models
✨ Massive Matrix Game Dataset with fine-grained action labels
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