Symbol-LLM (Symbol-LLM)

reacted to their post with 🚀🔥 about 13 hours ago

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320

🥳 Thrilled to introduce our recent efforts on bootstrapping VLMs for multi-modal chain-of-thought reasoning !

📕 Title: Vision-Language Models Can Self-Improve Reasoning via Reflection

🔗 Link: Vision-Language Models Can Self-Improve Reasoning via Reflection (2411.00855)

😇Takeaways:

- We found that VLMs can self-improve reasoning performance through a reflection mechanism, and importantly, this approach can scale through test-time computing.

- Evaluation on comprehensive and diverse Vision-Language reasoning tasks are included !

posted an update about 13 hours ago

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320

🥳 Thrilled to introduce our recent efforts on bootstrapping VLMs for multi-modal chain-of-thought reasoning !

📕 Title: Vision-Language Models Can Self-Improve Reasoning via Reflection

🔗 Link: Vision-Language Models Can Self-Improve Reasoning via Reflection (2411.00855)

😇Takeaways:

- We found that VLMs can self-improve reasoning performance through a reflection mechanism, and importantly, this approach can scale through test-time computing.

- Evaluation on comprehensive and diverse Vision-Language reasoning tasks are included !

reacted to maxiw's post with 🚀👍 9 days ago

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1709

Exciting to see open-source models thriving in the computer agent space! 🔥
I just built a demo for OS-ATLAS: A Foundation Action Model For Generalist GUI Agents — check it out here: maxiw/OS-ATLAS

This demo predicts bounding boxes based on screenshot + instructions as input.

reacted to their post with 🚀🔥 17 days ago

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2103

🚀 Excited to introduce a new member of the OS-Copilot family: OS-Atlas - an open-sourced foundational action model for GUI agents

📘 Paper: OS-ATLAS: A Foundation Action Model for Generalist GUI Agents (2410.23218)
🔗 Website: https://osatlas.github.io

😇 TL;DR: OS-Atlas offers:
1. State-of-the-Art GUI Grounding: Helps GUI agents accurately locate GUI elements.
2. Strong OOD Performance and Cross-platform Compatibility: Excels in out-of-domain agentic tasks across MacOS, Windows, Linux, Android, and Web.
3. Complete Infrastructure for GUI Data Synthesis:
You can easily build your own OS agent upon it!

posted an update 17 days ago

Post

2103

🚀 Excited to introduce a new member of the OS-Copilot family: OS-Atlas - an open-sourced foundational action model for GUI agents

📘 Paper: OS-ATLAS: A Foundation Action Model for Generalist GUI Agents (2410.23218)
🔗 Website: https://osatlas.github.io

😇 TL;DR: OS-Atlas offers:
1. State-of-the-Art GUI Grounding: Helps GUI agents accurately locate GUI elements.
2. Strong OOD Performance and Cross-platform Compatibility: Excels in out-of-domain agentic tasks across MacOS, Windows, Linux, Android, and Web.
3. Complete Infrastructure for GUI Data Synthesis:
You can easily build your own OS agent upon it!

reacted to their post with 🚀🤗 4 months ago

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2116

🔥Thrilled to release our 8B version of Symbol-LLM-Instruct !

It follows the two-stage training strategy proposed in the original paper and is continually optimized on LLaMA3-Chat-8B model.

Symbol-LLM was accepted by ACL'24 main conference ! See you in Thailand !

Paper link: https://arxiv.org/abs/2311.09278
Paper Title: Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models

1 reply

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posted an update 4 months ago

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2116

🔥Thrilled to release our 8B version of Symbol-LLM-Instruct !

It follows the two-stage training strategy proposed in the original paper and is continually optimized on LLaMA3-Chat-8B model.

Symbol-LLM was accepted by ACL'24 main conference ! See you in Thailand !

Paper link: https://arxiv.org/abs/2311.09278
Paper Title: Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models

1 reply

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replied to their post 4 months ago

Thanks for your positive feedback ! 🥳

reacted to FeYuan's post with 🚀 5 months ago

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4752

Hi everyone,

I am excited to introduce our latest work, LLaMAX. 😁😁😁

LLaMAX is a powerful language model created specifically for multilingual scenarios. Built upon Meta's LLaMA series models, LLaMAX undergoes extensive training across more than 100 languages.

Remarkably, it enhances its multilingual capabilities without compromising its generalization ability, surpassing existing LLMs.

✨Highlights:

🎈 LLaMAX supports the 102 languages covered by Flores-101, and its performance in translating between low-resource languages far surpasses other decoder-only LLMs.

🎈 Even for languages not covered in Flores-200, LLaMAX still shows significant improvements in translation performance.

🎈 By performing simple SFT on English task data, LLaMAX demonstrates impressive multilingual transfer abilities in downstream tasks.

🎈 In our paper, we discuss effective methods for enhancing the multilingual capabilities of LLMs during the continued training phase.

We welcome you to use our model and provide feedback.

More Details:

🎉 Code: https://github.com/CONE-MT/LLaMAX/

🎉 Model: https://huggingface.co/LLaMAX/

3 replies

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reacted to their post with 🚀🔥 5 months ago

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1911

📍Excited to make public a series of checkpoints !

- Final checkpoints after self-training with ENVISIONS framework
- Cover math, logic, and agent domains
- Include 7B / 13B

📕 Check our paper:
Title: Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models
Link: https://arxiv.org/abs/2406.11736

2 replies

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posted an update 5 months ago

Post

1911

📍Excited to make public a series of checkpoints !

- Final checkpoints after self-training with ENVISIONS framework
- Cover math, logic, and agent domains
- Include 7B / 13B

📕 Check our paper:
Title: Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models
Link: https://arxiv.org/abs/2406.11736

2 replies

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reacted to their post with 🔥🚀 5 months ago

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1777

📣Thrilled to make public our recent work ENVISIONS !!!

- Without human annotations !
- Without Distilling Strong LLMs !
- Self-improve LLMs in the environment
- Amazing performances on agentic and reasoning tasks
- Insightful analysis on "why" questions

📝 Title: Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models

📎 Repo: https://github.com/xufangzhi/ENVISIONS

1 reply

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posted an update 5 months ago

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1777

📣Thrilled to make public our recent work ENVISIONS !!!

- Without human annotations !
- Without Distilling Strong LLMs !
- Self-improve LLMs in the environment
- Amazing performances on agentic and reasoning tasks
- Insightful analysis on "why" questions

📝 Title: Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models

📎 Repo: https://github.com/xufangzhi/ENVISIONS

1 reply

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reacted to their post with 🚀 8 months ago

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1734

Check out our work Symbol-LLM ! We have open-sourced both 7B / 13B model weights, as well as part of the symbolic collections ! Try it !

Paper link: Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models (2311.09278)

Model weights: Symbol-LLM/Symbol-LLM-7B-Instruct

Symbol-LLM

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