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Image To Vector
š
Vectorizer AI | Convert Image to SVG
Konrad Szafer
KonradSzafer
AI & ML interests
Foundation Models, RL, Continual Learning
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Iāve been experimenting with a āTech Treeā to make ML research more systematic and transparentāturns out it helped me spot hidden interactions between experiments and share progress more easily. I wrote a short blog post with examples and insights! https://huggingface.co./spaces/KonradSzafer/tech_tree_blog
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Iāve been experimenting with a āTech Treeā to make ML research more systematic and transparentāturns out it helped me spot hidden interactions between experiments and share progress more easily. I wrote a short blog post with examples and insights!
KonradSzafer/tech_tree_blog
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Open-R1: a fully open reproduction of DeepSeek-R1
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Yesterday Ā
@mattshumer
released
mattshumer/Reflection-Llama-3.1-70B, an impressive model that achieved incredible results in benchmarks like MMLU. The model was fine-tuned using Reflection-Tuning and the dataset used wasn't released, but I created a small recipe with distilabel that allows generating a dataset with a similar output format:
1. We use MagPie š¦ in combination with https://huggingface.co./meta-llama/Meta-Llama-3.1-70B-Instruct to generate reasoning instructions.
2. We generate a response again using https://huggingface.co./meta-llama/Meta-Llama-3.1-70B-Instruct, but we steer the LLM to generate an specific output format using a custom system prompt. In the system prompt, we instruct the LLM that it will have first to think š and have reflections that will help resolving ambiguities. After that, we instruct the LLM to generate an output based on the previous thinking
In this dataset gabrielmbmb/distilabel-reflection-tuning you can found 5 rows that I generated with this recipe. You can also found the code of the pipeline in the file called
1. We use MagPie š¦ in combination with https://huggingface.co./meta-llama/Meta-Llama-3.1-70B-Instruct to generate reasoning instructions.
2. We generate a response again using https://huggingface.co./meta-llama/Meta-Llama-3.1-70B-Instruct, but we steer the LLM to generate an specific output format using a custom system prompt. In the system prompt, we instruct the LLM that it will have first to think š and have reflections that will help resolving ambiguities. After that, we instruct the LLM to generate an output based on the previous thinking
In this dataset gabrielmbmb/distilabel-reflection-tuning you can found 5 rows that I generated with this recipe. You can also found the code of the pipeline in the file called
reflection.py.
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