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anakin87ย
updated
a
Space
5 days ago
robert
#30 opened about 2 months ago
by
arvindparihar
Post
3474
๐ ๐๐ฟ๐ฎ๐ถ๐ป๐ฒ๐ฑ ๐ฎ ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐น ๐๐ผ ๐๐ฐ๐ต๐ฒ๐ฑ๐๐น๐ฒ ๐ฒ๐๐ฒ๐ป๐๐ ๐๐ถ๐๐ต ๐๐ฅ๐ฃ๐ข! ๐ ๐๏ธ
โ๏ธ 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 ๐๐ฒ๐ฎ๐ฐ๐ต๐ถ๐ป๐ด ๐ฎ ๐บ๐ผ๐ฑ๐ฒ๐น ๐๐ผ ๐ฐ๐ฟ๐ฒ๐ฎ๐๐ฒ ๐ฎ ๐๐ฐ๐ต๐ฒ๐ฑ๐๐น๐ฒ ๐ณ๐ฟ๐ผ๐บ ๐ฎ ๐น๐ถ๐๐ ๐ผ๐ณ ๐ฒ๐๐ฒ๐ป๐๐ ๐ฎ๐ป๐ฑ ๐ฝ๐ฟ๐ถ๐ผ๐ฟ๐ถ๐๐ถ๐ฒ๐.
Choosing an original problem forced me to:
๐ค Think about the problem setting
๐งฌ Generate data
๐ค Choose the right base model
๐ Design reward functions (and experiencing reward hacking)
๐ Run multiple rounds of training, hoping that my model would learn something.
A fun and rewarding ๐ experience.
I learned a lot of things, that I want to share with you. ๐
โ๏ธ Blog post: https://huggingface.co/blog/anakin87/qwen-scheduler-grpo
๐ป Code: https://github.com/anakin87/qwen-scheduler-grpo
๐ค Hugging Face collection (dataset and model): anakin87/qwen-scheduler-grpo-680bcc583e817390525a8837
โ๏ธ 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 ๐๐ฒ๐ฎ๐ฐ๐ต๐ถ๐ป๐ด ๐ฎ ๐บ๐ผ๐ฑ๐ฒ๐น ๐๐ผ ๐ฐ๐ฟ๐ฒ๐ฎ๐๐ฒ ๐ฎ ๐๐ฐ๐ต๐ฒ๐ฑ๐๐น๐ฒ ๐ณ๐ฟ๐ผ๐บ ๐ฎ ๐น๐ถ๐๐ ๐ผ๐ณ ๐ฒ๐๐ฒ๐ป๐๐ ๐ฎ๐ป๐ฑ ๐ฝ๐ฟ๐ถ๐ผ๐ฟ๐ถ๐๐ถ๐ฒ๐.
Choosing an original problem forced me to:
๐ค Think about the problem setting
๐งฌ Generate data
๐ค Choose the right base model
๐ Design reward functions (and experiencing reward hacking)
๐ Run multiple rounds of training, hoping that my model would learn something.
A fun and rewarding ๐ experience.
I learned a lot of things, that I want to share with you. ๐
โ๏ธ Blog post: https://huggingface.co/blog/anakin87/qwen-scheduler-grpo
๐ป Code: https://github.com/anakin87/qwen-scheduler-grpo
๐ค Hugging Face collection (dataset and model): anakin87/qwen-scheduler-grpo-680bcc583e817390525a8837
- 2 replies
Post
1744
๐๐๐ฐ ๐๐ญ๐๐ฅ๐ข๐๐ง ๐๐ฆ๐๐ฅ๐ฅ ๐๐๐ง๐ ๐ฎ๐๐ ๐ ๐๐จ๐๐๐ฅ๐ฌ: ๐๐๐ฆ๐ฆ๐ ๐๐๐จ๐ ๐๐ง๐๐ฌ๐ข๐ฌ ๐๐จ๐ฅ๐ฅ๐๐๐ญ๐ข๐จ๐ง ๐๐๐ฎ๐น
I am happy to release two new language models for the Italian Language!
๐ช 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.
๐ 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 ๐
๐ค 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.
๐ 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.
๐ Training code: https://www.kaggle.com/code/anakin87/post-training-gemma-for-italian-and-beyond
๐ Thanks @FinancialSupport and mii-llm for the help during evaluation.
I am happy to release two new language models for the Italian Language!
๐ช 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.
๐ 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 ๐
๐ค 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.
๐ 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.
๐ Training code: https://www.kaggle.com/code/anakin87/post-training-gemma-for-italian-and-beyond
๐ Thanks @FinancialSupport and mii-llm for the help during evaluation.
-
- 3 replies
Post
568
Hey, it has been a while... I was busy participating in ๐ ๐๐๐ฆ๐ฆ๐ ๐๐จ๐ฆ๐ฉ๐๐ญ๐ข๐ญ๐ข๐จ๐ง!
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: ๐๐๐ฎ๐น ๐๐๐จ๐ ๐๐ง๐๐ฌ๐ข๐ฌ - ๐๐จ๐ฌ๐ญ-๐๐ซ๐๐ข๐ง๐ข๐ง๐ ๐๐๐ฆ๐ฆ๐ ๐๐จ๐ซ ๐๐ญ๐๐ฅ๐ข๐๐ง ๐๐ง๐ ๐๐๐ฒ๐จ๐ง๐
๐ 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.
๐๐ฆ๐บ ๐๐ต๐ฆ๐ฑ๐ด
๐ Choose reference metrics
๐งโ๐ฌ Data curation for Instruction Fine Tuning: identify existing datasets + generate synthetic data
๐๏ธโโ๏ธ Efficient Instruction Fine Tuning with Spectrum
๐งโ๐ฌ Data curation for Preference Tuning: identify existing datasets + generate synthetic data
๐๐ Efficient Direct Preference Optimization with Spectrum
๐ Evaluation
๐ค Hugging Face collection (with models and datasets): anakin87/gemma-neogenesis-67824b7bf13ac9cfe091fe2e
I'm also planning a ๐ 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! ๐ป
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: ๐๐๐ฎ๐น ๐๐๐จ๐ ๐๐ง๐๐ฌ๐ข๐ฌ - ๐๐จ๐ฌ๐ญ-๐๐ซ๐๐ข๐ง๐ข๐ง๐ ๐๐๐ฆ๐ฆ๐ ๐๐จ๐ซ ๐๐ญ๐๐ฅ๐ข๐๐ง ๐๐ง๐ ๐๐๐ฒ๐จ๐ง๐
๐ 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.
๐๐ฆ๐บ ๐๐ต๐ฆ๐ฑ๐ด
๐ Choose reference metrics
๐งโ๐ฌ Data curation for Instruction Fine Tuning: identify existing datasets + generate synthetic data
๐๏ธโโ๏ธ Efficient Instruction Fine Tuning with Spectrum
๐งโ๐ฌ Data curation for Preference Tuning: identify existing datasets + generate synthetic data
๐๐ Efficient Direct Preference Optimization with Spectrum
๐ Evaluation
๐ค Hugging Face collection (with models and datasets): anakin87/gemma-neogenesis-67824b7bf13ac9cfe091fe2e
I'm also planning a ๐ 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! ๐ป
Post
1669
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 ๐ anakin87/tulu-3-sft-mixture-with-language
Unfortunately, it was missing the "language" column.
I added it using the good old fastText.
Check out the dataset here ๐ anakin87/tulu-3-sft-mixture-with-language
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- 1 reply
Post
443
๐๐๐ ๐ ๐๐ฐ๐๐ซ๐ฆ ๐จ๐ ๐๐ ๐๐ง๐ญ๐ฌ ๐ฐ๐ข๐ญ๐ก ๐๐ฅ๐๐ฆ๐ 3.2, ๐๐๐-4๐จ ๐ฆ๐ข๐ง๐ข ๐๐ง๐ ๐๐ฅ๐๐ฎ๐๐ 3.5 ๐๐จ๐ง๐ง๐๐ญ
๐๐;๐๐: I reimplemented the Swarm concept using Haystack, but made it work with both open and proprietary models ๐ซ
โ๏ธ blog article: https://haystack.deepset.ai/blog/swarm-of-agents
๐ 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:
ใป ๐๐จ๐ฎ๐ญ๐ข๐ง๐๐ฌ: Each agent follows specific ๐ instructions and uses ๐ ๏ธ tools to execute them.
ใป ๐๐๐ง๐๐จ๐๐๐ฌ ๐ค: Agents can transfer control to one another using tool/function calling.
When I first read these ideas, I thought: ๐ด๐ช๐ฎ๐ฑ๐ญ๐ฆ ๐ฃ๐ถ๐ต ๐ฑ๐ฐ๐ธ๐ฆ๐ณ๐ง๐ถ๐ญ! And they pair well with the recent unified tool support in Haystack.
๐งโ๐ป So, I decided to re-implement these concepts using Haystack, and in just a few lines of code, I had a working prototype.
๐ 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:
๐ Triage Agent - Llama 3.2 running on Ollama
๐ Sales Agent - Anthropic Claude 3.5 Sonnet
๐ 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! โจ
๐๐;๐๐: I reimplemented the Swarm concept using Haystack, but made it work with both open and proprietary models ๐ซ
โ๏ธ blog article: https://haystack.deepset.ai/blog/swarm-of-agents
๐ 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:
ใป ๐๐จ๐ฎ๐ญ๐ข๐ง๐๐ฌ: Each agent follows specific ๐ instructions and uses ๐ ๏ธ tools to execute them.
ใป ๐๐๐ง๐๐จ๐๐๐ฌ ๐ค: Agents can transfer control to one another using tool/function calling.
When I first read these ideas, I thought: ๐ด๐ช๐ฎ๐ฑ๐ญ๐ฆ ๐ฃ๐ถ๐ต ๐ฑ๐ฐ๐ธ๐ฆ๐ณ๐ง๐ถ๐ญ! And they pair well with the recent unified tool support in Haystack.
๐งโ๐ป So, I decided to re-implement these concepts using Haystack, and in just a few lines of code, I had a working prototype.
๐ 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:
๐ Triage Agent - Llama 3.2 running on Ollama
๐ Sales Agent - Anthropic Claude 3.5 Sonnet
๐ 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! โจ
Update README.md
1
#29 opened 7 months ago
by
Piterasny
Post
1111
Ok, you're finally convinced that synthetic data works... โ๏ธ
๐๐จ๐ฐ ๐ฒ๐จ๐ฎ ๐ฐ๐๐ง๐ญ ๐ญ๐จ ๐ ๐๐ง๐๐ซ๐๐ญ๐ ๐๐ง ๐ข๐ง๐ฌ๐ญ๐ซ๐ฎ๐๐ญ๐ข๐จ๐ง ๐๐๐ญ๐๐ฌ๐๐ญ ๐๐จ๐ซ ๐๐ข๐ง๐-๐ญ๐ฎ๐ง๐ข๐ง๐ ๐ข๐ง ๐ ๐ฅ๐๐ง๐ ๐ฎ๐๐ ๐ ๐จ๐ญ๐ก๐๐ซ ๐ญ๐ก๐๐ง ๐๐ง๐ ๐ฅ๐ข๐ฌ๐ก.
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
---
๐ฆโโฌ ๐๐ก๐๐ญ ๐ข๐ฌ ๐๐๐ ๐ฉ๐ข๐?
It's a recent technique for creating synthetic instruction datasets.
Magpie is based on a simple but ingenious idea ๐
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.
๐ช The authors demonstrate that using these datasets for Supervised Fine Tuning (SFT) can yield strong performance, even competitive with the original instruct model.
๐ง๐๐๐ง๐๐ซ๐๐ญ๐ข๐ง๐ ๐ง๐จ๐ง-๐๐ง๐ ๐ฅ๐ข๐ฌ๐ก ๐๐๐ญ๐
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!
โ Unfortunately, it does not work well for other languages (๐ฎ๐น, ๐ณ๐ฑ, ...)
๐
๐๐จ๐ฐ ๐ฒ๐จ๐ฎ ๐ฐ๐๐ง๐ญ ๐ญ๐จ ๐ ๐๐ง๐๐ซ๐๐ญ๐ ๐๐ง ๐ข๐ง๐ฌ๐ญ๐ซ๐ฎ๐๐ญ๐ข๐จ๐ง ๐๐๐ญ๐๐ฌ๐๐ญ ๐๐จ๐ซ ๐๐ข๐ง๐-๐ญ๐ฎ๐ง๐ข๐ง๐ ๐ข๐ง ๐ ๐ฅ๐๐ง๐ ๐ฎ๐๐ ๐ ๐จ๐ญ๐ก๐๐ซ ๐ญ๐ก๐๐ง ๐๐ง๐ ๐ฅ๐ข๐ฌ๐ก.
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
---
๐ฆโโฌ ๐๐ก๐๐ญ ๐ข๐ฌ ๐๐๐ ๐ฉ๐ข๐?
It's a recent technique for creating synthetic instruction datasets.
Magpie is based on a simple but ingenious idea ๐
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.
๐ช The authors demonstrate that using these datasets for Supervised Fine Tuning (SFT) can yield strong performance, even competitive with the original instruct model.
๐ง๐๐๐ง๐๐ซ๐๐ญ๐ข๐ง๐ ๐ง๐จ๐ง-๐๐ง๐ ๐ฅ๐ข๐ฌ๐ก ๐๐๐ญ๐
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!
โ Unfortunately, it does not work well for other languages (๐ฎ๐น, ๐ณ๐ฑ, ...)
๐
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- 1 reply
anakin87ย
updated
a
model
8 months ago
Post
1767
๐ต๐ป ๐๐ ๐๐ง๐ญ๐ข๐ ๐๐๐ ๐ฐ๐ข๐ญ๐ก ๐ฆ ๐๐ฅ๐๐ฆ๐ 3.2
I was excited to explore Llama 3.2, but as a simple ๐ช๐บ EU guy, I don't have access to Meta's multimodal models ๐ฟ
๐ค So I thought: why not challenge the small 3B text model with Agentic RAG?
๐ฏ 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: ๐ https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/llama32_agentic_rag.ipynb
My stack:
๐๏ธ haystack (https://haystack.deepset.ai/): open-source LLM orchestration framework
๐ฆ meta-llama/Llama-3.2-3B-Instruct
๐ฆ๐ free DuckDuckGo API, integrated with Haystack
โจ ๐๐ฉ๐ฆ ๐ณ๐ฆ๐ด๐ถ๐ญ๐ต๐ด? ๐๐ฏ๐ค๐ฐ๐ถ๐ณ๐ข๐จ๐ช๐ฏ๐จ - ๐ข ๐ง๐ฆ๐ธ ๐ฎ๐ฐ๐ฏ๐ต๐ฉ๐ด ๐ข๐จ๐ฐ, ๐ต๐ฉ๐ช๐ด ๐ญ๐ฆ๐ท๐ฆ๐ญ ๐ฐ๐ง ๐ฑ๐ฆ๐ณ๐ง๐ฐ๐ณ๐ฎ๐ข๐ฏ๐ค๐ฆ ๐ง๐ณ๐ฐ๐ฎ ๐ข ๐ด๐ฎ๐ข๐ญ๐ญ ๐ฎ๐ฐ๐ฅ๐ฆ๐ญ ๐ธ๐ฐ๐ถ๐ญ๐ฅ'๐ท๐ฆ ๐ฃ๐ฆ๐ฆ๐ฏ ๐ถ๐ฏ๐ต๐ฉ๐ช๐ฏ๐ฌ๐ข๐ฃ๐ญ๐ฆ!
This probably reflects the impressive IFEval score of the model (comparable to Llama 3.1 8B).
I was excited to explore Llama 3.2, but as a simple ๐ช๐บ EU guy, I don't have access to Meta's multimodal models ๐ฟ
๐ค So I thought: why not challenge the small 3B text model with Agentic RAG?
๐ฏ 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: ๐ https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/llama32_agentic_rag.ipynb
My stack:
๐๏ธ haystack (https://haystack.deepset.ai/): open-source LLM orchestration framework
๐ฆ meta-llama/Llama-3.2-3B-Instruct
๐ฆ๐ free DuckDuckGo API, integrated with Haystack
โจ ๐๐ฉ๐ฆ ๐ณ๐ฆ๐ด๐ถ๐ญ๐ต๐ด? ๐๐ฏ๐ค๐ฐ๐ถ๐ณ๐ข๐จ๐ช๐ฏ๐จ - ๐ข ๐ง๐ฆ๐ธ ๐ฎ๐ฐ๐ฏ๐ต๐ฉ๐ด ๐ข๐จ๐ฐ, ๐ต๐ฉ๐ช๐ด ๐ญ๐ฆ๐ท๐ฆ๐ญ ๐ฐ๐ง ๐ฑ๐ฆ๐ณ๐ง๐ฐ๐ณ๐ฎ๐ข๐ฏ๐ค๐ฆ ๐ง๐ณ๐ฐ๐ฎ ๐ข ๐ด๐ฎ๐ข๐ญ๐ญ ๐ฎ๐ฐ๐ฅ๐ฆ๐ญ ๐ธ๐ฐ๐ถ๐ญ๐ฅ'๐ท๐ฆ ๐ฃ๐ฆ๐ฆ๐ฏ ๐ถ๐ฏ๐ต๐ฉ๐ช๐ฏ๐ฌ๐ข๐ฃ๐ญ๐ฆ!
This probably reflects the impressive IFEval score of the model (comparable to Llama 3.1 8B).
julianrischย
updated
9
models
9 months ago
deepset/bert-base-german-cased-oldvocab
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deepset/gbert-base-germandpr-ctx_encoder
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deepset/tapas-large-nq-reader
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deepset/tapas-large-nq-hn-reader
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125
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deepset/gelectra-large-generator
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deepset/gelectra-base-generator
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deepset/gbert-base-germandpr-reranking
Text Classification
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10.2k
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6
deepset/gbert-base-germandpr-question_encoder
Feature Extraction
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deepset/gbert-large
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