Create synthetic data pipelines with easy!
- Retries and caching included
- inference via LiteLLM, vLLM, and popular batch APIs
- asynchronous operations
🔗 URL: buff.ly/ajPRT1l
Create synthetic data pipelines with easy!
- Retries and caching included
- inference via LiteLLM, vLLM, and popular batch APIs
- asynchronous operations
🔗 URL: buff.ly/ajPRT1l
token-explorer is a simple tool that lets you explore different possible paths that an LLM might sample!
- Arrow keys to navigate, pop and append tokens
- View the token probabilities and entropies.
GitHub: buff.ly/FQgsczM
token-explorer is a simple tool that lets you explore different possible paths that an LLM might sample!
- Arrow keys to navigate, pop and append tokens
- View the token probabilities and entropies.
GitHub: buff.ly/FQgsczM
💬 Natural language prompt to data
🦙 Ollama ensures secure local LLM inference
✍🏼 Argilla’s data curation capabilities complete the workflow
🔗 GitHub: buff.ly/5pX49Xc
💬 Natural language prompt to data
🦙 Ollama ensures secure local LLM inference
✍🏼 Argilla’s data curation capabilities complete the workflow
🔗 GitHub: buff.ly/5pX49Xc
🤗 Hugging Face - Dataset Studio is an amazing new feature.
🚀 Start yourself: buff.ly/pjpOKav
🤗 Hugging Face - Dataset Studio is an amazing new feature.
🚀 Start yourself: buff.ly/pjpOKav
🫸 New release to push vector search to the Hub and work with any serialisable objects.
🧑🏫 KNN, HNSW, USEARCH, ANNOY, PYNNDESCENT, FAISS, and VOYAGER.
🔗 Library:
🫸 New release to push vector search to the Hub and work with any serialisable objects.
🧑🏫 KNN, HNSW, USEARCH, ANNOY, PYNNDESCENT, FAISS, and VOYAGER.
🔗 Library:
🎨 Gradio released "gradio sketch"
🚼 Really easy way to create web apps with minimal code.
⚙️ Start with `pip install gradio` & `gradio sketch`
📒 Release: https://buff.ly/41aeLoA
🎨 Gradio released "gradio sketch"
🚼 Really easy way to create web apps with minimal code.
⚙️ Start with `pip install gradio` & `gradio sketch`
📒 Release: https://buff.ly/41aeLoA
<100K records, no problem!
>100K, some scaling issues
ANN DuckDB index, sub-second response times
Notebook:
<100K records, no problem!
>100K, some scaling issues
ANN DuckDB index, sub-second response times
Notebook:
💻 Code agents optimised for software development
🔧 Tool calling agents that create modular, function-driven workflows
🔍 Retrieval agents designed to access and synthesise information
Course: https://buff.ly/4kcj6Ai
💻 Code agents optimised for software development
🔧 Tool calling agents that create modular, function-driven workflows
🔍 Retrieval agents designed to access and synthesise information
Course: https://buff.ly/4kcj6Ai
Got data problems? Relax. Synthetic data is here to help.
Talk: https://buff.ly/3QzoZKj
Got data problems? Relax. Synthetic data is here to help.
Talk: https://buff.ly/3QzoZKj
🔥 Build genuinely useful datasets using natural language!
⚖️ Scale however you need.
🔐 Use them privately or share them with the world!
🧑💻 GitHub: https://buff.ly/49IDSmd
🔥 Build genuinely useful datasets using natural language!
⚖️ Scale however you need.
🔐 Use them privately or share them with the world!
🧑💻 GitHub: https://buff.ly/49IDSmd
You can now search and find the perfect agents and tools for your needs!
Powered by @Gradio!
Start searching:
You can now search and find the perfect agents and tools for your needs!
Powered by @Gradio!
Start searching:
1. Describe your desired image🎨
2. Two anonymous models output images
3. Vote for the winner!
Images have been sourced from our Open Image Preference dataset!
Dataset: https://buff.ly/4il0du9
Arena: https://buff.ly/4142NwH
1. Describe your desired image🎨
2. Two anonymous models output images
3. Vote for the winner!
Images have been sourced from our Open Image Preference dataset!
Dataset: https://buff.ly/4il0du9
Arena: https://buff.ly/4142NwH
We just released a bonus unit on function calling (FC).
You will learn:
⑴ What is FC?
⑵ Thought → Act → Observe Cycle in FC
⑶ lightweight and efficient fine-tuning
Course: https://buff.ly/3Qn1DHB
We just released a bonus unit on function calling (FC).
You will learn:
⑴ What is FC?
⑵ Thought → Act → Observe Cycle in FC
⑶ lightweight and efficient fine-tuning
Course: https://buff.ly/3Qn1DHB
library: https://buff.ly/4hj6PrJ
slides: https://buff.ly/3WUzZ8D
video:
library: https://buff.ly/4hj6PrJ
slides: https://buff.ly/3WUzZ8D
video:
I created the Tools gallery, which makes tools specifically developed by/for smolagents searchable and visible. This will help with:
- inspiration
- best practices
- finding cool tools
Space: https://buff.ly/41cYctx
I created the Tools gallery, which makes tools specifically developed by/for smolagents searchable and visible. This will help with:
- inspiration
- best practices
- finding cool tools
Space: https://buff.ly/41cYctx
Join the cohort of 66K students: https://buff.ly/4hxb6rK
Join the cohort of 66K students: https://buff.ly/4hxb6rK
Outlines has an integration with transformers, which facilitates structured generation based on limiting token sampling probabilities.
Blog: https://buff.ly/4jFHMkr
Outlines has an integration with transformers, which facilitates structured generation based on limiting token sampling probabilities.
Blog: https://buff.ly/4jFHMkr
We would love to hear your thoughts!
PR: https://buff.ly/4hRMny6
We would love to hear your thoughts!
PR: https://buff.ly/4hRMny6
smolagents effortlessness combined with the power of 400,000 AI tools available on the Hub!
library: https://buff.ly/4hj6PrJ
smolagents effortlessness combined with the power of 400,000 AI tools available on the Hub!
library: https://buff.ly/4hj6PrJ
And it actually works.
Available in French-English but super excited to see what the community will do.
Hub: https://buff.ly/3EtmM0f
Paper: https://buff.ly/4jIXNGd
And it actually works.
Available in French-English but super excited to see what the community will do.
Hub: https://buff.ly/3EtmM0f
Paper: https://buff.ly/4jIXNGd
Get familiar with the Agents and tools, not the bells and whistles!
Retrieve - Augment and now GENERATE.
Parts:
1: https://buff.ly/40XNIxM
2: https://buff.ly/40HkB0x
3:
Get familiar with the Agents and tools, not the bells and whistles!
Retrieve - Augment and now GENERATE.
Parts:
1: https://buff.ly/40XNIxM
2: https://buff.ly/40HkB0x
3:
Describe your dataset for RAG, LLMs or Text Classification
Bring your own context!
Press play and wait
Space: https://buff.ly/3Y1S99z
GitHub: https://buff.ly/49IDSmd
Describe your dataset for RAG, LLMs or Text Classification
Bring your own context!
Press play and wait
Space: https://buff.ly/3Y1S99z
GitHub: https://buff.ly/49IDSmd
Agentic RAG stack part 2 - augment
Augment retrieval results by reranking optimises content without increasing time too much
part2: https://buff.ly/40HkB0x
part1: https://buff.ly/40XNIxM
code: https://buff.ly/4hEajpj
Agentic RAG stack part 2 - augment
Augment retrieval results by reranking optimises content without increasing time too much
part2: https://buff.ly/40HkB0x
part1: https://buff.ly/40XNIxM
code: https://buff.ly/4hEajpj
1. go to https://buff.ly/42CnUbU
2. search the app you like
3. go to the bottom settings
4. open the URL
5. press the search bar to install
More info: https://buff.ly/3Csqc2J
1. go to https://buff.ly/42CnUbU
2. search the app you like
3. go to the bottom settings
4. open the URL
5. press the search bar to install
More info: https://buff.ly/3Csqc2J