deepset (deepset)

anakin87

posted an update 10 days ago

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4598

Want to quickly try Gemma 3 270m? 💎💬

I made a simple Space to do that: anakin87/gemma-3-270m-it

⚡ Fast: Flash Attention, Zero GPU
⚙️ Configurable

anakin87

posted an update 12 days ago

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358

🕵️🌐 Building Browser Agents - notebook

No API? No problem.
Browser Agents can use websites like you do: click, type, wait, read.

📓 Step-by-step notebook: https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/browser_agents.ipynb

🎥 In the video, the Agent:
- Goes to Hugging Face Spaces
- Finds black-forest-labs/FLUX.1-schnell
- Expands a short prompt ("my holiday on Lake Como") into a detailed image generation prompt
- Waits for the image
- Returns the image URL

## What else can it do?
Great for information gathering and summarization

🗞️🗞️ Compare news websites and create a table of shared stories with links
▶️ Find content creator social profiles from YouTube videos
🛍️ Find a product's price range on Amazon
🚂 🚌 Gather public transportation travel options

## How is it built?
🏗️ Haystack → Agent execution logic
🧠 Google Gemini 2.5 Flash → Good and fast LLM with a generous free tier
🛠️ Playwright MCP server → Browser automation tools: navigate, click, type, wait...

Even without vision capabilities, this setup can get quite far.

## Next steps
- Try a local open model
- Move from notebook to real deployment
- Incorporate vision

And you? Have you built something similar? What's in your stack?

anakin87

posted an update 19 days ago

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1059

Haystack can now see 👀

The latest release of the Haystack OSS LLM framework adds a long-requested feature: image support!

📓 Notebooks below

This isn't just about passing images to an LLM. We built several features to enable practical multimodal use cases.

What's new?
🧠 Support for multiple LLM providers: OpenAI, Amazon Bedrock, Google Gemini, Mistral, NVIDIA, OpenRouter, Ollama and more (support for Hugging Face API coming 🔜)
🎛️ Prompt template language to handle structured inputs, including images
📄 PDF and image converters
🔍 Image embedders using CLIP-like models
🧾 LLM-based extractor to pull text from images
🧩 Components to build multimodal RAG pipelines and Agents

I had the chance of leading this effort with @sjrhuschlee (great collab).

📓 Below you can find two notebooks to explore the new features:
󠁯•󠁏󠁏 Introduction to Multimodal Text Generation https://haystack.deepset.ai/cookbook/multimodal_intro
󠁯•󠁏󠁏 Creating Vision+Text RAG Pipelines https://haystack.deepset.ai/tutorials/46_multimodal_rag

(🖼️ image by @bilgeyucel )

anakin87

posted an update about 2 months ago

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🛡️ AI Guardrails with Open Language Models - Tutorial

📓 https://haystack.deepset.ai/cookbook/safety_moderation_open_lms

How do you ensure your AI application is safe from harmful or inappropriate user inputs?

This is a core requirement for real-world AI deployments. Luckily, several open Language Models are built specifically for safety moderation.

I've been exploring them and put together a hands-on tutorial using the Haystack framework to build your own AI guardrails.

In the notebook, you'll learn how to use and customize:
🔹 Meta Llama Guard (via Hugging Face API)
🔹 IBM Granite Guardian (via Ollama), which can also evaluate RAG specific risk dimensions
🔹 Google ShieldGemma (via Ollama)
🔹 Nvidia NemoGuard models family, including a model for topic control

You'll also see how to integrate content moderation into a 🔎 RAG pipeline.

anakin87

posted an update about 2 months ago

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1163

🧰 Free up space on the Hub with super_squash_history 🧹

As you may know, Hugging Face Hub has storage limits on private repos (100 GB for free users, 1 TB for PROs).

This weekend I did some cleanup on my private repos
I went 1.58 TB down to 1 GB. 😅

Besides deleting old, unused models, the main tool I used was a lesser-known command:
super_squash_history.

When you train a model, you often push multiple checkpoints to the Hub.
Each checkpoint = a commit.
A 2.6B model in BF16 is ~5 GB.
So 10 checkpoints = 50 GB. That adds up fast.

While full commit history can be useful for rollbacks, it's often unnecessary for older experiments where only the final model matters.

In these cases, you can use super_squash_history: it reduces your entire repo history to a single commit.

https://huggingface.co/docs/huggingface_hub/main/en/package_reference/hf_api#huggingface_hub.HfApi.super_squash_history

⚠️ super_squash_history is a non-revertible operation. Once squashed, the commit history cannot be retrieved.

Hope this is useful to others.

2 replies

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anakin87

updated a Space 2 months ago

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anakin87

in deepset/roberta-base-squad2 4 months ago

robert

#30 opened 4 months ago by

arvindparihar

anakin87

posted an update 4 months ago

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3502

𝗜 𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗮 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 𝘁𝗼 𝘀𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝗲𝘃𝗲𝗻𝘁𝘀 𝘄𝗶𝘁𝗵 𝗚𝗥𝗣𝗢! 👑 🗓️

✍️ Blog post: https://huggingface.co/blog/anakin87/qwen-scheduler-grpo

I experimented with GRPO lately.

I am fascinated by models learning from prompts and rewards - no example answers needed like in Supervised Fine-Tuning.

After the DeepSeek boom, everyone is trying GRPO with GSM8K or the Countdown Game...

I wanted a different challenge, like 𝘁𝗲𝗮𝗰𝗵𝗶𝗻𝗴 𝗮 𝗺𝗼𝗱𝗲𝗹 𝘁𝗼 𝗰𝗿𝗲𝗮𝘁𝗲 𝗮 𝘀𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝗳𝗿𝗼𝗺 𝗮 𝗹𝗶𝘀𝘁 𝗼𝗳 𝗲𝘃𝗲𝗻𝘁𝘀 𝗮𝗻𝗱 𝗽𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝗲𝘀.

Choosing an original problem forced me to:
🤔 Think about the problem setting
🧬 Generate data
🤏 Choose the right base model
🏆 Design reward functions (and experiencing reward hacking)
🔄 Run multiple rounds of training, hoping that my model would learn something.

A fun and rewarding 😄 experience.

I learned a lot of things, that I want to share with you. 👇
✍️ Blog post: https://huggingface.co/blog/anakin87/qwen-scheduler-grpo
💻 Code: https://github.com/anakin87/qwen-scheduler-grpo
🤗 Hugging Face collection (dataset and model): anakin87/qwen-scheduler-grpo-680bcc583e817390525a8837

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anakin87

posted an update 7 months ago

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𝐍𝐞𝐰 𝐈𝐭𝐚𝐥𝐢𝐚𝐧 𝐒𝐦𝐚𝐥𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬: 𝐆𝐞𝐦𝐦𝐚 𝐍𝐞𝐨𝐠𝐞𝐧𝐞𝐬𝐢𝐬 𝐜𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧 💎🌍🇮🇹

I am happy to release two new language models for the Italian Language!

💪 Gemma 2 9B Neogenesis ITA
anakin87/gemma-2-9b-neogenesis-ita
Building on the impressive work by VAGO Solutions, I applied Direct Preference Optimization with a mix of Italian and English data.
Using Spectrum, I trained 20% of model layers.

📊 Evaluated on the Open ITA LLM leaderboard ( mii-llm/open_ita_llm_leaderboard), this model achieves strong performance.
To beat it on this benchmark, you'd need a 27B model 😎

🤏 Gemma 2 2B Neogenesis ITA
anakin87/gemma-2-2b-neogenesis-ita
This smaller variant is fine-tuned from the original Gemma 2 2B it by Google.
Through a combination of Supervised Fine-Tuning and Direct Preference Optimization, I trained 25% of the layers using Spectrum.

📈 Compared to the original model, it shows improved Italian proficiency, good for its small size.

Both models were developed during the recent #gemma competition on Kaggle.
📓 Training code: https://www.kaggle.com/code/anakin87/post-training-gemma-for-italian-and-beyond

🙏 Thanks @FinancialSupport and mii-llm for the help during evaluation.

3 replies

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anakin87

posted an update 7 months ago

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573

Hey, it has been a while... I was busy participating in 💎 𝐆𝐞𝐦𝐦𝐚 𝐜𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐢𝐨𝐧!

Here's the idea: Gemma open models have a large vocabulary size (256K), so improving them for a specific language or cultural context should be pretty affordable - no need for continued pre-training.

My submission: 💎🌍🇮🇹 𝐍𝐞𝐨𝐠𝐞𝐧𝐞𝐬𝐢𝐬 - 𝐏𝐨𝐬𝐭-𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐆𝐞𝐦𝐦𝐚 𝐟𝐨𝐫 𝐈𝐭𝐚𝐥𝐢𝐚𝐧 𝐚𝐧𝐝 𝐛𝐞𝐲𝐨𝐧𝐝
📓 Kaggle notebook: https://www.kaggle.com/code/anakin87/post-training-gemma-for-italian-and-beyond

In this notebook, I show how I improve the performance of Gemma 2 2B on Italian via Post-Training.
I believe this method is adaptable to other languages and model sizes.

𝘒𝘦𝘺 𝘚𝘵𝘦𝘱𝘴
📊 Choose reference metrics
🧑‍🔬 Data curation for Instruction Fine Tuning: identify existing datasets + generate synthetic data
🏋️‍♂️ Efficient Instruction Fine Tuning with Spectrum
🧑‍🔬 Data curation for Preference Tuning: identify existing datasets + generate synthetic data
👍👎 Efficient Direct Preference Optimization with Spectrum
📈 Evaluation

🤗 Hugging Face collection (with models and datasets): anakin87/gemma-neogenesis-67824b7bf13ac9cfe091fe2e

I'm also planning a 🎁 Gemma Giveaway (on LinkedIn - https://www.linkedin.com/in/stefano-fiorucci) in the next few days - sharing techniques, datasets, and models I used for my project... so stay tuned! 📻

anakin87

posted an update 9 months ago

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1673

Tulu 3 SFT Mixture by AllenAI is a massive, good, multilingual dataset for fine-tuning Language Models.

Unfortunately, it was missing the "language" column.

I added it using the good old fastText.

Check out the dataset here 👉 anakin87/tulu-3-sft-mixture-with-language

1 reply

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anakin87

posted an update 9 months ago

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444

🐝🐝🐝 𝐀 𝐒𝐰𝐚𝐫𝐦 𝐨𝐟 𝐀𝐠𝐞𝐧𝐭𝐬 𝐰𝐢𝐭𝐡 𝐋𝐥𝐚𝐦𝐚 3.2, 𝐆𝐏𝐓-4𝐨 𝐦𝐢𝐧𝐢 𝐚𝐧𝐝 𝐂𝐥𝐚𝐮𝐝𝐞 3.5 𝐒𝐨𝐧𝐧𝐞𝐭

𝐓𝐋;𝐃𝐑: I reimplemented the Swarm concept using Haystack, but made it work with both open and proprietary models 💫

✍️ blog article: https://haystack.deepset.ai/blog/swarm-of-agents
📓 notebook: https://haystack.deepset.ai/cookbook/swarm

Some time ago OpenAI published Swarm: an educational framework for building multi-agent systems.

Their approach focuses on two main concepts:
・ 𝐑𝐨𝐮𝐭𝐢𝐧𝐞𝐬: Each agent follows specific 📜 instructions and uses 🛠️ tools to execute them.
・ 𝐇𝐚𝐧𝐝𝐨𝐟𝐟𝐬 🤝: Agents can transfer control to one another using tool/function calling.

When I first read these ideas, I thought: 𝘴𝘪𝘮𝘱𝘭𝘦 𝘣𝘶𝘵 𝘱𝘰𝘸𝘦𝘳𝘧𝘶𝘭! And they pair well with the recent unified tool support in Haystack.

🧑‍💻 So, I decided to re-implement these concepts using Haystack, and in just a few lines of code, I had a working prototype.

🆒 Bonus feature: this implementation isn't tied to a single model provider - different agents can be powered by different models!

I replicated the ACME customer service example from the original article, with 3 Agents:
🐝 Triage Agent - Llama 3.2 running on Ollama
🐝 Sales Agent - Anthropic Claude 3.5 Sonnet
🐝 Issues and Repairs Agent - OpenAI GPT-4o mini

Want to see the full implementation and give it a try? Check out the blog post and notebook! ✨

anakin87

in deepset/roberta-base-squad2 9 months ago

Update README.md

1

#29 opened 9 months ago by

Piterasny

anakin87

posted an update 10 months ago

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1113

Ok, you're finally convinced that synthetic data works... ⚗️

𝐍𝐨𝐰 𝐲𝐨𝐮 𝐰𝐚𝐧𝐭 𝐭𝐨 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐞 𝐚𝐧 𝐢𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐨𝐧 𝐝𝐚𝐭𝐚𝐬𝐞𝐭 𝐟𝐨𝐫 𝐟𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠 𝐢𝐧 𝐚 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐨𝐭𝐡𝐞𝐫 𝐭𝐡𝐚𝐧 𝐄𝐧𝐠𝐥𝐢𝐬𝐡.
But how do you get started?

I explore how to do this with Magpie in my new article
https://huggingface.co/blog/anakin87/multilingual-magpie

---

🐦‍⬛ 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐌𝐚𝐠𝐩𝐢𝐞?

It's a recent technique for creating synthetic instruction datasets.

Magpie is based on a simple but ingenious idea 👇
if you prompt an instruction-tuned model with a pre-query template, you can make it generate a plausible user query/instruction

Here's an example:
model: Llama-3-8B-Instruct
pre-query template: "<|begin_of_text|><|start_header_id|>user<|end_header_id|>"
generated user instruction: "What are some of the responsibilities of a commercial pilot?"

You can then feed this instruction back into the same model to get the assistant response.

By repeating this process, it's possible to generate large synthetic datasets with relatively little effort.

🪄 The authors demonstrate that using these datasets for Supervised Fine Tuning (SFT) can yield strong performance, even competitive with the original instruct model.

🧗𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐧𝐨𝐧-𝐄𝐧𝐠𝐥𝐢𝐬𝐡 𝐝𝐚𝐭𝐚

Most Language Models are primarily trained on English texts, so they tend to produce data in English.

How can we overcome this?

Earlier approaches were complex or costly.

Then @mrm8488 found a simple solution: add the target language to the pre-query template.
For Spanish, the template becomes "<|begin_of_text|><|start_header_id|>user<|end_header_id|>spanish:".

This method works for Spanish and German!

❌ Unfortunately, it does not work well for other languages (🇮🇹, 🇳🇱, ...)

👇

1 reply

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anakin87

updated a model 10 months ago

deepset/deberta-v3-base-injection

Text Classification • 0.2B • Updated Oct 15, 2024 • 88.8k • • 38

anakin87

posted an update 11 months ago

Post

1769

🕵🏻 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐑𝐀𝐆 𝐰𝐢𝐭𝐡 🦙 𝐋𝐥𝐚𝐦𝐚 3.2

I was excited to explore Llama 3.2, but as a simple 🇪🇺 EU guy, I don't have access to Meta's multimodal models 😿

🤔 So I thought: why not challenge the small 3B text model with Agentic RAG?

🎯 The plan:
- Build a system that tries to answer questions using a knowledge base.
- If the documents don't contain the answer, use Web search for additional context.

Check out my experimental notebook here: 📓 https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/llama32_agentic_rag.ipynb

My stack:
🏗️ haystack (https://haystack.deepset.ai/): open-source LLM orchestration framework
🦙 meta-llama/Llama-3.2-3B-Instruct
🦆🌐 free DuckDuckGo API, integrated with Haystack

✨ 𝘛𝘩𝘦 𝘳𝘦𝘴𝘶𝘭𝘵𝘴? 𝘌𝘯𝘤𝘰𝘶𝘳𝘢𝘨𝘪𝘯𝘨 - 𝘢 𝘧𝘦𝘸 𝘮𝘰𝘯𝘵𝘩𝘴 𝘢𝘨𝘰, 𝘵𝘩𝘪𝘴 𝘭𝘦𝘷𝘦𝘭 𝘰𝘧 𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦 𝘧𝘳𝘰𝘮 𝘢 𝘴𝘮𝘢𝘭𝘭 𝘮𝘰𝘥𝘦𝘭 𝘸𝘰𝘶𝘭𝘥'𝘷𝘦 𝘣𝘦𝘦𝘯 𝘶𝘯𝘵𝘩𝘪𝘯𝘬𝘢𝘣𝘭𝘦!
This probably reflects the impressive IFEval score of the model (comparable to Llama 3.1 8B).

julianrisch

updated 4 models 11 months ago

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