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Undi95 
posted an update 2 days ago
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3184
Exciting news!

After a long wait, Ikari and me finally made a new release of our last model on NeverSleep repo: Lumimaid-v0.2

This model can be used in different size, from the small Llama-3.1-8B to the gigantic Mistral-Large-123B, finetuned by us.

Try them now!

- NeverSleep/Lumimaid-v0.2-8B
- NeverSleep/Lumimaid-v0.2-12B
- NeverSleep/Lumimaid-v0.2-70B
- NeverSleep/Lumimaid-v0.2-123B

All the datasets we used will be added and credit will be given!
For the quant, we wait for fix to be applied (https://github.com/ggerganov/llama.cpp/pull/8676)
Hope you will enjoy them!
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nroggendorff 
posted an update 1 day ago
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1482
Datasets are down, I offer a solution

git lfs install

git clone https://huggingface.co/datasets/{dataset/id}

from datasets import load_dataset

dataset = load_dataset("id")
merve 
posted an update 2 days ago
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2362
At Hugging Face we have an open-source Cookbook with many applied AI recipes 📖
Here are some of the latest recipes contributed ⥥

- "Information Extraction with Haystack and NuExtract": Use Haystack and transformers to build structured data extraction pipelines using LLMs by @anakin87 https://huggingface.co/learn/cookbook/en/information_extraction_haystack_nuextract

- "Build RAG with Hugging Face and Milvus": Learn how to use Milvus with sentence transformers to build RAG pipelines https://huggingface.co/learn/cookbook/rag_with_hf_and_milvus

- "Code Search with Vector Embeddings and Qdrant": Search a codebase by building a retrieval pipeline using Qdrant and sentence transformers https://huggingface.co/learn/cookbook/code_search

- Data analyst agent: get your data’s insights in the blink of an eye ✨: great recipe by our own @m-ric showing how to build an agent that can do data analysis! 😱 https://huggingface.co/learn/cookbook/agent_data_analyst
as-cle-bert 
posted an update about 20 hours ago
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471
Hi HF Community!🤗

In the past days, OpenAI announced their search engine, SearchGPT: today, I'm glad to introduce you SearchPhi, an AI-powered and open-source web search tool that aims to reproduce similar features to SearchGPT, built upon microsoft/Phi-3-mini-4k-instruct, llama.cpp🦙 and Streamlit.
Although not as capable as SearchGPT, SearchPhi v0.0-beta.0 is a first step toward a fully functional and multimodal search engine :)
If you want to know more, head over to the GitHub repository (https://github.com/AstraBert/SearchPhi) and, to test it out, use this HF space: as-cle-bert/SearchPhi
Have fun!🐱
singhsidhukuldeep 
posted an update 2 days ago
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1512
Hello, HuggingFace🤗 community 🌟,

All the amazing people quantising LLMs to AWQ and GPTQ 🔧🤖

Can you please mention the perplexity you achieved 📉 OR any other metric to measure the quantisation qualitatively? 📊

The GGUF community follows this really well! 👍

And if it is not too much to ask, the script used for quantisation would be amazing! 📝

Thanks for the quants for the GPU poor! 💻
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davidberenstein1957 
posted an update 2 days ago
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1055
⚗️ Find reusable synthetic data pipeline code and corresponding datasets on the @huggingface Hub.

Find your pipline and use $ distilabel pipeline run --config "hugging_face_dataset_url/pipeline.yaml"

Some components I used
- Embedded dataset viewer https://huggingface.co/docs/hub/main/en/datasets-viewer-embed
- Hugging Face fsspec https://huggingface.co/docs/huggingface_hub/main/en/guides/hf_file_system
- distilabel https://distilabel.argilla.io/latest/
- Gradio leaderboard by Freddy Boulton freddyaboulton/gradio_leaderboard
- Gradio modal by Ali Abid

Space: davidberenstein1957/distilabel-synthetic-data-pipeline-explorer
rwightman 
posted an update 2 days ago
m-ric 
posted an update 3 days ago
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1763
𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝘁: 𝗱𝗿𝗼𝗽 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗳𝗶𝗹𝗲, 𝗹𝗲𝘁 𝘁𝗵𝗲 𝗟𝗟𝗠 𝗱𝗼 𝘁𝗵𝗲 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 📊⚙️

Need to make quick exploratory data analysis? ➡️ Get help from an agent.

I was impressed by Llama-3.1's capacity to derive insights from data. Given a csv file, it makes quick work of exploratory data analysis and can derive interesting insights.

On the data from the Kaggle titanic challenge, that records which passengers survived the Titanic wreckage, it was able by itself to derive interesting trends like "passengers that paid higher fares were more likely to survive" or "survival rate was much higher for women than men".

The cookbook even lets the agent built its own submission to the challenge, and it ranks under 3,000 out of 17,000 submissions: 👏 not bad at all!

Try it for yourself in this Space demo 👉 m-ric/agent-data-analyst
grimjim 
posted an update 3 days ago
ehristoforu 
posted an update 3 days ago
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1884
😏 Hello from Project Fluently Team!

✨ Finally we can give you some details about Supple Diffusion. We worked on it for a long time and we have little left, we apologize that we had to increase the work time.

🛠️ Some technical information. The first version will be the Small version (there will also be Medium, Large, Huge, possibly Tiny), it will be based on the SD1 architecture, that is, one text encoder, U-net, VAE. Now about each component, the first is a text encoder, it will be a CLIP model (perhaps not CLIP-L-path14), CLIP was specially retrained by us in order to achieve the universality of the model in understanding completely different styles and to simplify the prompt as much as possible. Next, we did U-net, U-net in a rather complicated way, first we trained different parts (types) of data with different U-nets, then we carried out merging using different methods, then we trained DPO and SPO using methods, and then we looked at the remaining shortcomings and further trained model, details will come later. We left VAE the same as in SD1 architecture.

🙌 Compatibility. Another goal of the Supple model series is full compatibility with Auto1111 and ComfyUI already at the release stage, the model is fully supported by these interfaces and the diffusers library and does not require adaptation, your usual Sampling methods are also compatible, such as DPM++ 2M Karras, DPM++ SDE and others.

🧐 Today, without demo images (there wasn’t much time), final work is underway on the model and we are already preparing to develop the Medium version, the release of the Small version will most likely be in mid-August or earlier.

😻 Feel free to ask your questions in the comments below the post, we will be happy to answer them, have a nice day!
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