Qwen/Qwen2.5-Coder-32B-Instruct
Text Generation
โข
Updated
โข
72.6k
โข
โข
985
weiguosun
chuangxinlezhi
ยท
AI & ML interests
None yet
Recent Activity
liked
a model
4 days ago
Qwen/Qwen2.5-Coder-32B-Instruct
liked
a model
about 1 month ago
nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
Organizations
chuangxinlezhi's activity
liked
a
model
4 days ago
Reacted to
m-ric's
post with ๐
15 days ago
Post
1617
๐๐ป๐ฑ๐ฟ๐ผ๐ถ๐ฑ๐๐ฎ๐ฏ: ๐๐ถ๐ฟ๐๐ ๐ฒ๐๐ฒ๐ฟ ๐๐๐๐๐ฒ๐บ๐ฎ๐๐ถ๐ฐ ๐ฏ๐ฒ๐ป๐ฐ๐ต๐บ๐ฎ๐ฟ๐ธ ๐ณ๐ผ๐ฟ ๐๐ป๐ฑ๐ฟ๐ผ๐ถ๐ฑ ๐บ๐ผ๐ฏ๐ถ๐น๐ฒ ๐ฎ๐ด๐ฒ๐ป๐๐ ๐๐ต๐ผ๐๐ ๐๐ต๐ฎ๐ ๐๐บ๐ฎ๐น๐น, ๐ณ๐ถ๐ป๐ฒ-๐๐๐ป๐ฒ๐ฑ ๐ผ๐ฝ๐ฒ๐ป ๐บ๐ผ๐ฑ๐ฒ๐น๐ ๐ฐ๐ฎ๐ป ๐ฝ๐ผ๐๐ฒ๐ฟ ๐ฎ ๐๐๐ฅ๐ฉ๐๐ฆ ๐๐๐๐๐ฒ๐บ ๐ผ๐ป ๐๐ผ๐๐ฟ ๐๐บ๐ฎ๐ฟ๐๐ฝ๐ต๐ผ๐ป๐ฒ ๐ฑ๐ฅ
A team from Tsinghua University just released AndroidLab, the first systematic framework to evaluate and train Android mobile agents that works with both text-only and multimodal models.
They show that fine-tuning small open-source models can significantly boost performance, matching that of much bigger closed models like GPT-4o.
The team built:
๐ย A reproducible benchmark with 138 tasks across 9 apps to evaluate mobile agents systematically
๐๐ฑย A framework supporting both text-only (via XML) and visual (via marked screenshots) interfaces
โ ย An instruction dataset of 10.5k operation traces for training mobile agents
Key insights:
- ๐ Fine-tuning improves performance BY A LOT: Open-source model Llama-3.1-8B improves from 2% to 24% success rate after training, nearly reaching GPT-4o performance although itโs much smaller
- โ๏ธ Text-only agents match multimodal ones: XML-based agents achieve similar performance to screenshot-based multimodal agents.
Read their paper here ๐ AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agents (2410.24024)
A team from Tsinghua University just released AndroidLab, the first systematic framework to evaluate and train Android mobile agents that works with both text-only and multimodal models.
They show that fine-tuning small open-source models can significantly boost performance, matching that of much bigger closed models like GPT-4o.
The team built:
๐ย A reproducible benchmark with 138 tasks across 9 apps to evaluate mobile agents systematically
๐๐ฑย A framework supporting both text-only (via XML) and visual (via marked screenshots) interfaces
โ ย An instruction dataset of 10.5k operation traces for training mobile agents
Key insights:
- ๐ Fine-tuning improves performance BY A LOT: Open-source model Llama-3.1-8B improves from 2% to 24% success rate after training, nearly reaching GPT-4o performance although itโs much smaller
- โ๏ธ Text-only agents match multimodal ones: XML-based agents achieve similar performance to screenshot-based multimodal agents.
Read their paper here ๐ AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agents (2410.24024)
liked
a
model
about 1 month ago
upvoted
an
article
3 months ago
Article
Local AI with Docker's Testcontainers
By
โข
โข
7
liked
a
model
4 months ago
Reacted to
fdaudens's
post with ๐ฅโค๏ธ๐ค
4 months ago
Post
1951
I just had a masterclass in open-source collaboration with the release of Llama 3.1 ๐ฆ๐ค
Meta dropped Llama 3.1, and seeing firsthand the Hugging Face team working to integrate it is nothing short of impressive. Their swift integration, comprehensive documentation, and innovative tools showcase the power of open-source teamwork.
For the curious minds:
๐ Check out independent evaluations: open-llm-leaderboard/open_llm_leaderboard
๐ง Deep dive into the tech: https://huggingface.co/blog/llama31
๐จโ๐ณ Try different recipes (including running 8B on free Colab!): https://github.com/huggingface/huggingface-llama-recipes
๐ Visualize open vs. closed LLM progress: andrewrreed/closed-vs-open-arena-elo
๐ค Generate synthetic data with distilabel, thanks to the new license allowing the use of outputs to train other LLMs https://huggingface.co/blog/llama31#synthetic-data-generation-with-distilabel
๐ก Pro tip: Experience the 405B version for free on HuggingChat, now with tool-calling capabilities! https://huggingface.co/chat/
#OpenSourceAI #AIInnovation
Meta dropped Llama 3.1, and seeing firsthand the Hugging Face team working to integrate it is nothing short of impressive. Their swift integration, comprehensive documentation, and innovative tools showcase the power of open-source teamwork.
For the curious minds:
๐ Check out independent evaluations: open-llm-leaderboard/open_llm_leaderboard
๐ง Deep dive into the tech: https://huggingface.co/blog/llama31
๐จโ๐ณ Try different recipes (including running 8B on free Colab!): https://github.com/huggingface/huggingface-llama-recipes
๐ Visualize open vs. closed LLM progress: andrewrreed/closed-vs-open-arena-elo
๐ค Generate synthetic data with distilabel, thanks to the new license allowing the use of outputs to train other LLMs https://huggingface.co/blog/llama31#synthetic-data-generation-with-distilabel
๐ก Pro tip: Experience the 405B version for free on HuggingChat, now with tool-calling capabilities! https://huggingface.co/chat/
#OpenSourceAI #AIInnovation
- 1 reply
updated
a
Space
4 months ago
Update README.md
#3 opened 4 months ago
by
chuangxinlezhi
chuangxinlezhi
updated
a
Space
4 months ago
Reacted to
sayakpaul's
post with ๐คฏ๐ฅ
4 months ago
Post
2191
Were you aware that we have a dedicated guide on different prompting mechanisms to improve the image generation quality? ๐งจ
Takes you through simple prompt engineering, prompt weighting, prompt enhancement using GPT-2, and more.
Check out the guide here ๐ฆฏ
https://huggingface.co/docs/diffusers/main/en/using-diffusers/weighted_prompts
Takes you through simple prompt engineering, prompt weighting, prompt enhancement using GPT-2, and more.
Check out the guide here ๐ฆฏ
https://huggingface.co/docs/diffusers/main/en/using-diffusers/weighted_prompts
- 1 reply
Reacted to
Severian's
post with ๐ค
4 months ago
Post
3258
GraphRAG-Ollama-UI
I've been working on a local version of Microsoft's GraphRAG that uses Ollama for everything. It's got a new interactive UI built with Gradio that makes it easier to manage data, run queries, and visualize results. It's not fully featured or set up to harness the entire GraphRAG library yet but it allows you to run all the standard commands for Indexing/Processing and chatting with your graph. Some key features:
Uses local models via Ollama for LLM and embeddings
3D graph visualization of the knowledge graph using Plotly
File management through the UI (upload, view, edit, delete)
Settings management in the interface
Real-time logging for debugging
https://github.com/severian42/GraphRAG-Ollama-UI
I've been working on a local version of Microsoft's GraphRAG that uses Ollama for everything. It's got a new interactive UI built with Gradio that makes it easier to manage data, run queries, and visualize results. It's not fully featured or set up to harness the entire GraphRAG library yet but it allows you to run all the standard commands for Indexing/Processing and chatting with your graph. Some key features:
Uses local models via Ollama for LLM and embeddings
3D graph visualization of the knowledge graph using Plotly
File management through the UI (upload, view, edit, delete)
Settings management in the interface
Real-time logging for debugging
https://github.com/severian42/GraphRAG-Ollama-UI
- 1 reply
hjkhj
#7 opened 5 months ago
by
chuangxinlezhi
chuangxinlezhi
liked
a
model
5 months ago
liked
a
Space
5 months ago
liked
2
models
5 months ago
1
#2 opened 6 months ago
by
chuangxinlezhi
chuangxinlezhi