GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
Paper
•
1811.10597
•
Published
Spaces-explorers
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hen
authored
6
papers
3 months ago
Semantic Photo Manipulation with a Generative Image Prior
Paper
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2005.07727
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Published
Understanding the Role of Individual Units in a Deep Neural Network
Paper
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2009.05041
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Published
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Paper
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2102.01672
•
Published
LeGrad: An Explainability Method for Vision Transformers via Feature Formation Sensitivity
Paper
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2404.03214
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Published
•
2
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations
Paper
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2403.06009
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Published
gabrielmbmb
authored
a
paper
4 months ago
stellaathena
authored
2
papers
4 months ago
gabrielmbmb
posted
an
update
9 months ago
Post
1907
Yesterday
@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 🐦 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 💭 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 🐦 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 💭 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.
gabrielmbmb
posted
an
update
10 months ago
Post
2929
distilabel 1.3.0 is out! This release contains many core improvements and new tasks that help us building
argilla/magpie-ultra-v0.1!
Distributed pipeline execution with Ray, new Magpie tasks, reward models, components for dataset diversity based on sentence embeddings, Argilla 2.0 compatibility and many more features!
Check the new release in GitHub: https://github.com/argilla-io/distilabel
Distributed pipeline execution with Ray, new Magpie tasks, reward models, components for dataset diversity based on sentence embeddings, Argilla 2.0 compatibility and many more features!
Check the new release in GitHub: https://github.com/argilla-io/distilabel
gabrielmbmb
posted
an
update
10 months ago
Post
3602
Just dropped magpie-ultra-v0.1! The first open synthetic dataset generated with Llama 3.1 405B. Created with distilabel, it's our most advanced and compute-intensive pipeline to date. We made the GPUs of the cluster go brrrrr 🚀
argilla/magpie-ultra-v0.1
Take it a look and tell us what you think! Probably, the models taking the most out of it are smol models 🤗 We will be improving the dataset in upcoming iterations!
argilla/magpie-ultra-v0.1
Take it a look and tell us what you think! Probably, the models taking the most out of it are smol models 🤗 We will be improving the dataset in upcoming iterations!
Wataru
authored
a
paper
10 months ago
gabrielmbmb
posted
an
update
11 months ago
Post
2508
⚗️ distilabel 1.2.0 is out and it comes with improved support for structured generation, new tasks for generating datasets for training embedding models, new steps for loading data, MixtureOfAgentsLLM and improved docs.
We would love to see a few new datasets for training embedding models built with distilabel on the Hub! ❤️
We would love to see a few new datasets for training embedding models built with distilabel on the Hub! ❤️
stellaathena
authored
a
paper
12 months ago
stellaathena
authored
a
paper
about 1 year ago
Post
ICYMI! Nomic Embed v1.5: Resizable Production Embeddings with Matryoshka Representation Learning
- Variable embedding dimension from 64 <-> 768
- Outperforms text-embedding-ada-002 while achieving a 3x memory reduction
- Day 1 integrations with Langchain, LlamaIndex, MongoDB, and Sentence Transformers
Check out
nomic-ai/nomic-embed-text-v1.5 for the model weights.
Technical report: https://static.nomic.ai/reports/2024_Nomic_Embed_Text_Technical_Report.pdf
Blog Post: https://blog.nomic.ai/posts/nomic-embed-matryoshka
Original Tweet Thread: https://x.com/nomic_ai/status/1757782157374734665?s=20
- Variable embedding dimension from 64 <-> 768
- Outperforms text-embedding-ada-002 while achieving a 3x memory reduction
- Day 1 integrations with Langchain, LlamaIndex, MongoDB, and Sentence Transformers
Check out
nomic-ai/nomic-embed-text-v1.5 for the model weights.
Technical report: https://static.nomic.ai/reports/2024_Nomic_Embed_Text_Technical_Report.pdf
Blog Post: https://blog.nomic.ai/posts/nomic-embed-matryoshka
Original Tweet Thread: https://x.com/nomic_ai/status/1757782157374734665?s=20
stellaathena
authored
3
papers
over 1 year ago