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ehcalabres  updated a dataset 16 days ago
hf-dell-internal/image-checksums
ehcalabres  published a dataset 23 days ago
hf-dell-internal/image-checksums
alvarobartt  published a dataset 2 months ago
hf-dell-internal/container-scans
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juanjucm 
posted an update 8 days ago
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172
Last week,
zai-org dropped zai-org/GLM-4.7-Flash. Now, we bring it to Microsoft Foundry!

- 🏆 30B-A3B MoE, the strongest model in the 30B class. It excels at coding tasks, agentic workflows and reasoning.
- 🤏🏻 Lighter version of his 358B big brother, balancing performance and efficiency.

Not light enough for you? We are also adding
unsloth unsloth/GLM-4.7-Flash-GGUF to the catalog, with GPU and CPU support powered by llama.cpp 🔥

Go join the hype and deploy them from the Hugging Face collection on Microsoft Foundry!
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alvarobartt 
posted an update 8 days ago
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2823
💥 hf-mem v0.4.1 now also estimates KV cache memory requirements for any context length and batch size with the --experimental flag!

uvx hf-mem --model-id ... --experimental will automatically pull the required information from the Hugging Face Hub to include the KV cache estimation, when applicable.

💡 Alternatively, you can also set the --max-model-len, --batch-size and --kv-cache-dtype arguments (à la vLLM) manually if preferred.
  • 1 reply
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ehcalabres 
updated a dataset 16 days ago
ehcalabres 
published a dataset 23 days ago
alvarobartt 
published a dataset 2 months ago
alvarobartt 
posted an update 11 months ago
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3634
🔥 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)
alvarobartt 
posted an update over 1 year ago
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3038
🤗 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
alvarobartt 
posted an update over 1 year ago
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3275
🔥 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 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
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alvarobartt 
posted an update almost 2 years ago
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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 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
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🚀 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!
ehcalabres 
authored a paper almost 2 years ago
alvarobartt 
posted an update about 2 years ago
alvarobartt 
posted an update about 2 years ago
ehcalabres 
authored a paper over 2 years ago