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alvarobartt
published
a
dataset
about 1 month ago
alvarobartt
posted
an
update
10 months ago
Post
3575
🔥 Agents can do anything!
@microsoft
Research just announced the release of Magma 8B!
Magma is a new Visual Language Model (VLM) with 8B parameters for multi-modal agents designed to handle complex interactions across virtual and real environments; and it's MIT licensed!
Magma comes with exciting new features such as:
- Introduces the Set-of-Mark and Trace-of-Mark techniques for fine-tuning
- Leverages a large amount of unlabeled video data to learn the spatial-temporal grounding and planning
- A strong generalization and ability to be fine-tuned for other agentic tasks
- SOTA in different multi-modal benchmarks spanning across UI navigation, robotics manipulation, image / video understanding and spatial understanding and reasoning
- Generates goal-driven visual plans and actions for agentic use cases
Model: microsoft/Magma-8B
Technical Report: Magma: A Foundation Model for Multimodal AI Agents (2502.13130)
Magma is a new Visual Language Model (VLM) with 8B parameters for multi-modal agents designed to handle complex interactions across virtual and real environments; and it's MIT licensed!
Magma comes with exciting new features such as:
- Introduces the Set-of-Mark and Trace-of-Mark techniques for fine-tuning
- Leverages a large amount of unlabeled video data to learn the spatial-temporal grounding and planning
- A strong generalization and ability to be fine-tuned for other agentic tasks
- SOTA in different multi-modal benchmarks spanning across UI navigation, robotics manipulation, image / video understanding and spatial understanding and reasoning
- Generates goal-driven visual plans and actions for agentic use cases
Model: microsoft/Magma-8B
Technical Report: Magma: A Foundation Model for Multimodal AI Agents (2502.13130)
alvarobartt
posted
an
update
over 1 year ago
Post
3037
🤗 Serving Meta Llama 3.1 405B on Google Cloud is now possible via the Hugging Face Deep Learning Containers (DLCs) for Text Generation Inference (TGI)
In this post, we showcase how to deploy https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct-FP8 on an A3 instance with 8 x H100 GPUs on Vertex AI
Thanks to the Hugging Face DLCs for TGI and Google Cloud Vertex AI, deploying a high-performance text generation container for serving Large Language Models (LLMs) has never been easier. And we’re not going to stop here – stay tuned as we enable more experiences to build AI with open models on Google Cloud!
Read the full post at https://huggingface.co/blog/llama31-on-vertex-ai
In this post, we showcase how to deploy https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct-FP8 on an A3 instance with 8 x H100 GPUs on Vertex AI
Thanks to the Hugging Face DLCs for TGI and Google Cloud Vertex AI, deploying a high-performance text generation container for serving Large Language Models (LLMs) has never been easier. And we’re not going to stop here – stay tuned as we enable more experiences to build AI with open models on Google Cloud!
Read the full post at https://huggingface.co/blog/llama31-on-vertex-ai
alvarobartt
posted
an
update
over 1 year ago
Post
3274
🔥 Prometheus 2 was recently released by Kaist AI as an alternative and closely mirroring both human and GPT-4 evaluation, and surpassing the former Prometheus!
prometheus-eval/prometheus-7b-v2.0
prometheus-eval/prometheus-8x7b-v2.0
🌬️Fine-tuned on top of mistralai/Mistral-7B-Instruct-v0.2 and mistralai/Mixtral-8x7B-Instruct-v0.1
🗂️The datasets used for fine-tuning have been publicly released i.e. prometheus-eval/Feedback-Collection and prometheus-eval/Preference-Collection
🤝🏻Unified LM evaluator for absolute (a single prompt-completion pair) and relative (two completions for a given prompt) due to model merging
❌No longer needs a mandatory reference / golden answer, but can still be provided optionally
🔝Surpasses the former version of Prometheus, and has a high correlation with human, GPT-4, and Claude 3 Opus scores when evaluating LMs
📝Apache 2.0 license
Long-story short, an amazing job from Kaist AI bridging the gap with LLM evaluators other than proprietary and bigger models!
This week at Argilla, we decided to add a new task to use Prometheus 2 as an LLM evaluator using
😱 Using
Find the generated dataset and the code at distilabel-internal-testing/instruction-dataset-prometheus
prometheus-eval/prometheus-7b-v2.0
prometheus-eval/prometheus-8x7b-v2.0
🌬️Fine-tuned on top of mistralai/Mistral-7B-Instruct-v0.2 and mistralai/Mixtral-8x7B-Instruct-v0.1
🗂️The datasets used for fine-tuning have been publicly released i.e. prometheus-eval/Feedback-Collection and prometheus-eval/Preference-Collection
🤝🏻Unified LM evaluator for absolute (a single prompt-completion pair) and relative (two completions for a given prompt) due to model merging
❌No longer needs a mandatory reference / golden answer, but can still be provided optionally
🔝Surpasses the former version of Prometheus, and has a high correlation with human, GPT-4, and Claude 3 Opus scores when evaluating LMs
📝Apache 2.0 license
Long-story short, an amazing job from Kaist AI bridging the gap with LLM evaluators other than proprietary and bigger models!
This week at Argilla, we decided to add a new task to use Prometheus 2 as an LLM evaluator using
distilabel, so we implemented PrometheusEval.😱 Using
PrometheusEval running their 7B variant with vLLM in a single L40 on top of
HuggingFaceH4/instruction-dataset, we got the 327 existing prompt-completion pairs evaluated and pushed to the Hub in less than 2 minutes!Find the generated dataset and the code at distilabel-internal-testing/instruction-dataset-prometheus
- 1 reply
alvarobartt
posted
an
update
over 1 year ago
Post
2779
🦫 We have just released
argilla/Capybara-Preferences in collaboration with Kaist AI (
@JW17
,
@nlee-208
) and Hugging Face (
@lewtun
)
A new synthetic preference dataset built using
The current dataset combines the already generated alternative completions from argilla/distilabel-capybara-dpo-7k-binarized, while also adding the remaining ones using the same approach!
Here are some key features on how we built it:
- 🧹 Duplicate removal, keeping the conversation besides the last assistant response, and some slight pre-processing
- 🤖 Generation of alternative completions for the existing conversations (last turn only) with: mlabonne/NeuralBeagle14-7B, argilla/notus-7b-v1, and teknium/OpenHermes-2.5-Mistral-7B
- 👨🏻🏫 Running UltraFeedback via GPT-4 to generate the critique i.e. ratings and rationales, for the last assistant responses
- 🎉 Finally, we selected the chosen and rejected responses based on their UltraFeedback score, and applied some slight post-processing!
Sounds simple right? Start building your own synthetic datasets with https://github.com/argilla-io/distilabel already!
A new synthetic preference dataset built using
distilabel on top of the awesome
LDJnr/Capybara from
@LDJnr
The current dataset combines the already generated alternative completions from argilla/distilabel-capybara-dpo-7k-binarized, while also adding the remaining ones using the same approach!
Here are some key features on how we built it:
- 🧹 Duplicate removal, keeping the conversation besides the last assistant response, and some slight pre-processing
- 🤖 Generation of alternative completions for the existing conversations (last turn only) with: mlabonne/NeuralBeagle14-7B, argilla/notus-7b-v1, and teknium/OpenHermes-2.5-Mistral-7B
- 👨🏻🏫 Running UltraFeedback via GPT-4 to generate the critique i.e. ratings and rationales, for the last assistant responses
- 🎉 Finally, we selected the chosen and rejected responses based on their UltraFeedback score, and applied some slight post-processing!
Sounds simple right? Start building your own synthetic datasets with https://github.com/argilla-io/distilabel already!
ehcalabres
posted
an
update
almost 2 years ago
Post
🚀 Hello HF Posts World!
I'm excited to share in my first HF post that we at Neuraptic AI have released MAGNUM, the first open-source AI model designed to natively support any structured and unstructured data modality.
MAGNUM can learn a holistic representation of your business logic from any source of digital information—be it images, documents, emails, databases, audio, signals, and more. This rich context empowers it to deliver significantly more accurate answers.
If you want to know more about it, feel free to ask or read the paper here 🤗
A Modular End-to-End Multimodal Learning Method for Structured and Unstructured Data (2403.04866)
Have a nice week!
I'm excited to share in my first HF post that we at Neuraptic AI have released MAGNUM, the first open-source AI model designed to natively support any structured and unstructured data modality.
MAGNUM can learn a holistic representation of your business logic from any source of digital information—be it images, documents, emails, databases, audio, signals, and more. This rich context empowers it to deliver significantly more accurate answers.
If you want to know more about it, feel free to ask or read the paper here 🤗
A Modular End-to-End Multimodal Learning Method for Structured and Unstructured Data (2403.04866)
Have a nice week!
ehcalabres
authored
a
paper
almost 2 years ago
alvarobartt
posted
an
update
about 2 years ago
Post
💬 Notux 8x7b has already its own Chat UI running on 🤗 Spaces! Feel free to give it a try and chat with Notux, and let us know how it goes.
https://huggingface.co/spaces/argilla/notux-chat-ui
Kudos to @gabrielmbmb !
https://huggingface.co/spaces/argilla/notux-chat-ui
Kudos to @gabrielmbmb !
alvarobartt
posted
an
update
about 2 years ago
Post
💨 Notux 8x7b was just released!
From Argilla, we recently fine-tuned Mixtral 8x7b Instruct from Mistral AI using DPO, and a binarized and curated version of UltraFeedback, to find out it outperforms every other MoE-based model on the Hub.
- argilla/notux-8x7b-v1
- argilla/ultrafeedback-binarized-preferences-cleaned
From Argilla, we recently fine-tuned Mixtral 8x7b Instruct from Mistral AI using DPO, and a binarized and curated version of UltraFeedback, to find out it outperforms every other MoE-based model on the Hub.
- argilla/notux-8x7b-v1
- argilla/ultrafeedback-binarized-preferences-cleaned
-
- 19 replies
ehcalabres
authored
a
paper
about 2 years ago