KittenML/kitten-tts-nano-0.1
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KittenML/kitten-tts-nano-0.1
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Hugging Face just made life easier with the new hf CLI!
huggingface-cli to hf
With renaming the CLI, there are new features added like hf jobs. We can now run any script or Docker image on dedicated Hugging Face infrastructure with a simple command. It's a good addition for running experiments and jobs on the fly.
To get started, just run:
pip install -U huggingface_hub
List of hf CLI Commands
Main Commands
hf auth: Manage authentication (login, logout, etc.).
hf cache: Manage the local cache directory.
hf download: Download files from the Hub.
hf jobs: Run and manage Jobs on the Hub.
hf repo: Manage repos on the Hub.
hf upload: Upload a file or a folder to the Hub.
hf version: Print information about the hf version.
hf env: Print information about the environment.
Authentication Subcommands (hf auth)
login: Log in using a Hugging Face token.
logout: Log out of your account.
whoami: See which account you are logged in as.
switch: Switch between different stored access tokens/profiles.
list: List all stored access tokens.
Jobs Subcommands (hf jobs)
run: Run a Job on Hugging Face infrastructure.
inspect: Display detailed information on one or more Jobs.
logs: Fetch the logs of a Job.
ps: List running Jobs.
cancel: Cancel a Job.
hashtag#HuggingFace hashtag#MachineLearning hashtag#AI hashtag#DeepLearning hashtag#MLTools hashtag#MLOps hashtag#OpenSource hashtag#Python hashtag#DataScience hashtag#DevTools hashtag#LLM hashtag#hfCLI hashtag#GenerativeAI
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20 days ago
prithivMLmods/OCR-ReportLab-Notebooks
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19 days ago
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Hugging Face just made life easier with the new hf CLI!
huggingface-cli to hf
With renaming the CLI, there are new features added like hf jobs. We can now run any script or Docker image on dedicated Hugging Face infrastructure with a simple command. It's a good addition for running experiments and jobs on the fly.
To get started, just run:
pip install -U huggingface_hub
List of hf CLI Commands
Main Commands
hf auth: Manage authentication (login, logout, etc.).
hf cache: Manage the local cache directory.
hf download: Download files from the Hub.
hf jobs: Run and manage Jobs on the Hub.
hf repo: Manage repos on the Hub.
hf upload: Upload a file or a folder to the Hub.
hf version: Print information about the hf version.
hf env: Print information about the environment.
Authentication Subcommands (hf auth)
login: Log in using a Hugging Face token.
logout: Log out of your account.
whoami: See which account you are logged in as.
switch: Switch between different stored access tokens/profiles.
list: List all stored access tokens.
Jobs Subcommands (hf jobs)
run: Run a Job on Hugging Face infrastructure.
inspect: Display detailed information on one or more Jobs.
logs: Fetch the logs of a Job.
ps: List running Jobs.
cancel: Cancel a Job.
hashtag#HuggingFace hashtag#MachineLearning hashtag#AI hashtag#DeepLearning hashtag#MLTools hashtag#MLOps hashtag#OpenSource hashtag#Python hashtag#DataScience hashtag#DevTools hashtag#LLM hashtag#hfCLI hashtag#GenerativeAI
huggingface-cli to hf
With renaming the CLI, there are new features added like hf jobs. We can now run any script or Docker image on dedicated Hugging Face infrastructure with a simple command. It's a good addition for running experiments and jobs on the fly.
To get started, just run:
pip install -U huggingface_hub
List of hf CLI Commands
Main Commands
hf auth: Manage authentication (login, logout, etc.).
hf cache: Manage the local cache directory.
hf download: Download files from the Hub.
hf jobs: Run and manage Jobs on the Hub.
hf repo: Manage repos on the Hub.
hf upload: Upload a file or a folder to the Hub.
hf version: Print information about the hf version.
hf env: Print information about the environment.
Authentication Subcommands (hf auth)
login: Log in using a Hugging Face token.
logout: Log out of your account.
whoami: See which account you are logged in as.
switch: Switch between different stored access tokens/profiles.
list: List all stored access tokens.
Jobs Subcommands (hf jobs)
run: Run a Job on Hugging Face infrastructure.
inspect: Display detailed information on one or more Jobs.
logs: Fetch the logs of a Job.
ps: List running Jobs.
cancel: Cancel a Job.
hashtag#HuggingFace hashtag#MachineLearning hashtag#AI hashtag#DeepLearning hashtag#MLTools hashtag#MLOps hashtag#OpenSource hashtag#Python hashtag#DataScience hashtag#DevTools hashtag#LLM hashtag#hfCLI hashtag#GenerativeAI
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Explore OCR, Captioning, and Visual Understanding with Cutting-Edge Models on Hugging Face. 🤗🧪
I’ve put together a collection of Google Colab notebooks to experiment with some of the most exciting models available on the Hugging Face Hub focused on OCR, image captioning, and visual understanding tasks. [Image-to-Text] / [Image-Text-to-Text]
> 📖 OCR-ReportLab-Notebooks : prithivMLmods/OCR-ReportLab-Notebooks
These notebooks are built for quick prototyping and run on free T4 GPUs, making them perfect for experimentation, testing ideas, or just exploring what’s possible with modern vision-language models.
Note: The experimental notebooks are compiled with models that fit within the T4 GPU (free-tier) limits. More models along with their notebooks will be added over time.
I’ve put together a collection of Google Colab notebooks to experiment with some of the most exciting models available on the Hugging Face Hub focused on OCR, image captioning, and visual understanding tasks. [Image-to-Text] / [Image-Text-to-Text]
> 📖 OCR-ReportLab-Notebooks : prithivMLmods/OCR-ReportLab-Notebooks
These notebooks are built for quick prototyping and run on free T4 GPUs, making them perfect for experimentation, testing ideas, or just exploring what’s possible with modern vision-language models.
Note: The experimental notebooks are compiled with models that fit within the T4 GPU (free-tier) limits. More models along with their notebooks will be added over time.
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Mind2Web 2 is out - this time featuring eval and benchmark for deep research🔥
Paper: Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judge (2506.21506)
Project: https://osu-nlp-group.github.io/Mind2Web-2/
Paper: Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judge (2506.21506)
Project: https://osu-nlp-group.github.io/Mind2Web-2/
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Hi everyone, we’ve got big news! Starting today, all Langfuse product features are available as free OSS (MIT license).
You can now upgrade your self-hosted Langfuse to access features like:
- Managed LLM-as-a-Judge evaluations
- Annotation queues
- Prompt experiments
- LLM playground
We’re incredibly grateful for the support of this amazing community and can’t wait to hear your feedback on the new features!
More on this change here: https://langfuse.com/blog/2025-06-04-open-sourcing-langfuse-product
You can now upgrade your self-hosted Langfuse to access features like:
- Managed LLM-as-a-Judge evaluations
- Annotation queues
- Prompt experiments
- LLM playground
We’re incredibly grateful for the support of this amazing community and can’t wait to hear your feedback on the new features!
More on this change here: https://langfuse.com/blog/2025-06-04-open-sourcing-langfuse-product
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