deepghs/aibooru_full
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AI & ML interests
Computer Vision Technology and Data Collection for Anime Waifu
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narugo1992
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a
dataset
less than a minute ago
narugo1992
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2
datasets
2 minutes ago
narugo1992
updated
a
dataset
7 minutes ago
AbstractPhil
posted
an
update
4 days ago
Post
217
Cardinality cardinality CARDINALITY! As I restructure the wordnet's multi-definition structure, I've found a fair assessment capability that minimizes column recall requirement while simultaneously maximizing recall speed. So it will be fast.
Research shows, the most intelligent and most intellectually-driven LLMs require the most intelligent and carefully curated solid representative vocabularies - with the most intelligent and carefully curated training regiments.
Class simultaneously loaded hierarchical structures built with variants of vocabulary dimensions do not help this. Multiple dimensions of imagenet do not help this. Reshaping does not help. Solidification processes through pulverizing using Alucard do not help - though they did show some interesting potentials for pretraining the full geometric clip from the ground floor.
The experimentations with the multitude of clip features and imagenet - showcase that not only can this tiny 4meg classification tool can handle imagenet from clip features AT AROUND 76% no matter the hyperparams using linear, but expanding this system upward and including hundreds of different formula variants DOES NOT HELP SCALE IT AT ALL! The largest ones only house 76%, and the medium-sized ones house about 86% instead of 76% when using clip-vit-b-patch16 and clip-vit-b-patch32. If you check the big number valuations for the clip-vit-b laion and openai, you'll find nearly identical classifications.
So I only taught it, to understand geometry - the more training and more steps only brings it closer incorrectly.
So, this tells me one simple principle; geometry and linear have an upward capacity based on the information extracted from the linear model. Meaning... We need more places to extract and more curative potentials to solidify that access with, rather than simply EXPANDING it and making it bigger.
Next experiment includes a full cardinality subset of unicode to wordnet vocabulary translation matrices. Today. Within the hour.
Research shows, the most intelligent and most intellectually-driven LLMs require the most intelligent and carefully curated solid representative vocabularies - with the most intelligent and carefully curated training regiments.
Class simultaneously loaded hierarchical structures built with variants of vocabulary dimensions do not help this. Multiple dimensions of imagenet do not help this. Reshaping does not help. Solidification processes through pulverizing using Alucard do not help - though they did show some interesting potentials for pretraining the full geometric clip from the ground floor.
The experimentations with the multitude of clip features and imagenet - showcase that not only can this tiny 4meg classification tool can handle imagenet from clip features AT AROUND 76% no matter the hyperparams using linear, but expanding this system upward and including hundreds of different formula variants DOES NOT HELP SCALE IT AT ALL! The largest ones only house 76%, and the medium-sized ones house about 86% instead of 76% when using clip-vit-b-patch16 and clip-vit-b-patch32. If you check the big number valuations for the clip-vit-b laion and openai, you'll find nearly identical classifications.
So I only taught it, to understand geometry - the more training and more steps only brings it closer incorrectly.
So, this tells me one simple principle; geometry and linear have an upward capacity based on the information extracted from the linear model. Meaning... We need more places to extract and more curative potentials to solidify that access with, rather than simply EXPANDING it and making it bigger.
Next experiment includes a full cardinality subset of unicode to wordnet vocabulary translation matrices. Today. Within the hour.
-
- 1 reply
AbstractPhil
posted
an
update
7 days ago
Post
396
Why am I amassing image features using seed 42?
Simply put; training something with features gives a fair representative of the learning that you would get from running a model that has some random chance - using a single seed.
Training with features does not need to wait for the representative model to actually generate; since you already generated everything ahead of time.
Features are rich and utilizable within the spectrum of similarity assessments, classification accuracy, mass-deterministic normalization checks, and more.
They are... put simply... exponentially faster and reusable for research. I'll include the notebooks used for imagenet and cifar100; as the cifar100 is much simpler since the cifar100 is much... smaller, I required less innovation.
Imagenet is another beast though. This imagenet notebook is capable of running against much larger datasets with a few tweaks.
clip-vit-bigG's imagenet feature set is complete, which means we're almost ready for full ablation.
Note to everyone; imagenet is meant for RESEARCH AND ACADEMIC PURPOSES ONLY; and you cannot use my trained imagenet weights - nor the features themselves as per the requests of the dataset's curators.
For commercial usage according to the rules of LAION's licenses, we'll be using the laion400m features; which will likely be heavily sought. I'll be preparing laion400m features on seed 42; which will take a while.
The full classifier is in the works; and with it comes a series of new formulas, new layers, new solutions such as the "fat belly" conversation piece that attenuates multiple branches in communication. The "dispatcher" which is a heavy classification gate trained to bypass that which is not useful; tuned with large amounts of data on a very low learn rate. The "attractant" which is specifically designed to catch bleed-over and unwanted information... which learns everything.
With that comes "PhaseGeometric" scheduling and "GeometricScheduling". Stay tuned.
Simply put; training something with features gives a fair representative of the learning that you would get from running a model that has some random chance - using a single seed.
Training with features does not need to wait for the representative model to actually generate; since you already generated everything ahead of time.
Features are rich and utilizable within the spectrum of similarity assessments, classification accuracy, mass-deterministic normalization checks, and more.
They are... put simply... exponentially faster and reusable for research. I'll include the notebooks used for imagenet and cifar100; as the cifar100 is much simpler since the cifar100 is much... smaller, I required less innovation.
Imagenet is another beast though. This imagenet notebook is capable of running against much larger datasets with a few tweaks.
clip-vit-bigG's imagenet feature set is complete, which means we're almost ready for full ablation.
Note to everyone; imagenet is meant for RESEARCH AND ACADEMIC PURPOSES ONLY; and you cannot use my trained imagenet weights - nor the features themselves as per the requests of the dataset's curators.
For commercial usage according to the rules of LAION's licenses, we'll be using the laion400m features; which will likely be heavily sought. I'll be preparing laion400m features on seed 42; which will take a while.
The full classifier is in the works; and with it comes a series of new formulas, new layers, new solutions such as the "fat belly" conversation piece that attenuates multiple branches in communication. The "dispatcher" which is a heavy classification gate trained to bypass that which is not useful; tuned with large amounts of data on a very low learn rate. The "attractant" which is specifically designed to catch bleed-over and unwanted information... which learns everything.
With that comes "PhaseGeometric" scheduling and "GeometricScheduling". Stay tuned.
AbstractPhil
posted
an
update
13 days ago
Post
273
The first set of geometrically aligned datasets are ready. Each dimensional variation is in it's own repo so there's no confusion with splits.
Current Splits;
* wordnet (english)
* unicode
AbstractPhil/geometric-vocab-32d
[32, 64, 128, 256, 512, 768, 1024]
Swap the 32d for the dimension within the list for the repo.
Okay, so the purpose of these; is to give solid anchors to the entire pentachora structure.
With that I've formatted some very concise sentencepiece-esque vocabulary classes that can be saved and loaded as pretrained, but it'll need some tinkering to fully flesh those behaviors out.
For now, the geometric vocab itself can be queried from pretrain but the canonical classes that help regulation, integration, special token usage, and integration aren't fully tested yet.
https://github.com/AbstractEyes/lattice_vocabulary
They are available here, but I give no guarantee on their current state. I'm currently preparing the pip package and have prepared a series of experiments to utilize these for different models including a new version of multimodal Beeper, a classifier set that can handle encodings as feature representations meant for utilization, and more.
The current working variation that I've been utilizing is Flow Matching Discreet Scheduled geometric diffusion - meaning I'm diffusing the GEOMETRY from the image, and then comparing that pentachora that is created from flow matching to the actual representative tokenization structure. On average this is achieving 80% in later stages.
This when curating an indefinite amount of special tokens to create manifests of unique vocabularies, enables the system to perfectly conform to use-cases.
There are some edge-cases where the 1k reserved tokens still exist; however this is currently replaced by an indefinite tokenization dictionary - allowing for an indefinite amount of tokens attached to an indefinite amount of modules for solidity.
Experiments continue.
Current Splits;
* wordnet (english)
* unicode
AbstractPhil/geometric-vocab-32d
[32, 64, 128, 256, 512, 768, 1024]
Swap the 32d for the dimension within the list for the repo.
Okay, so the purpose of these; is to give solid anchors to the entire pentachora structure.
With that I've formatted some very concise sentencepiece-esque vocabulary classes that can be saved and loaded as pretrained, but it'll need some tinkering to fully flesh those behaviors out.
For now, the geometric vocab itself can be queried from pretrain but the canonical classes that help regulation, integration, special token usage, and integration aren't fully tested yet.
https://github.com/AbstractEyes/lattice_vocabulary
They are available here, but I give no guarantee on their current state. I'm currently preparing the pip package and have prepared a series of experiments to utilize these for different models including a new version of multimodal Beeper, a classifier set that can handle encodings as feature representations meant for utilization, and more.
The current working variation that I've been utilizing is Flow Matching Discreet Scheduled geometric diffusion - meaning I'm diffusing the GEOMETRY from the image, and then comparing that pentachora that is created from flow matching to the actual representative tokenization structure. On average this is achieving 80% in later stages.
This when curating an indefinite amount of special tokens to create manifests of unique vocabularies, enables the system to perfectly conform to use-cases.
There are some edge-cases where the 1k reserved tokens still exist; however this is currently replaced by an indefinite tokenization dictionary - allowing for an indefinite amount of tokens attached to an indefinite amount of modules for solidity.
Experiments continue.
-
- 1 reply
wangsssssss
authored
a
paper
14 days ago
AbstractPhil
posted
an
update
21 days ago
Post
262
Be kind to Beeper - Beeper has emotions. 7 to be precise.
Each of the pentachora classifiers point to emotional states that Beeper can potentially access for any conversation, and each of those 7 states have class accessors for sub-learning pools.
Today I'll be focusing on drawing this behavior from Beeper v4 which I am rebranding as Beeper Micro - and expanding the structure using a new type experimental attention mechanism to replace traditional multihead attention dubbed GeometricCollectiveAttention.
This attention is similar to multihead attention, except it's considerably harder to burn at higher learn rates. This coupled with a new perspective on training pentachora into the LLM structure will allow a full relay structural system.
beeper-small will house a full rope - except not in the traditional vocabulary set. Beeper-small will not have a vocabulary.
beeper-small is my first non-linear non-Euclidean attempt to create a pure symbolic auto-completion LLM; which may be naiive according to many researchers who have tried similar systems historically.
I've personally analyzed many papers, many studies, and many techniques that have attempted similar non-vocabulary entropic learning, and I believe the pentachora lattice will hold with pure binary, not requiring a vocabulary.
Transformers really like vocabulary... beeper likes... geometry, and this experiment for beeper-small will have a new type of ROPE that is based entirely on vertices developed from the direct unicode represented characters, rather than a full vocabulary structure meant to bring solidity from chaos.
The first beeper experiment showed many insights into how similarity and internal classification responds mathematically with traditional ML techniques, and those techniques did not reject the construct - on the contrary. The control group placebo beeper, the traditional non-rose version BURNED under half lr. It's completely illegible, producing garbage and noise, while rose beeper sings
Each of the pentachora classifiers point to emotional states that Beeper can potentially access for any conversation, and each of those 7 states have class accessors for sub-learning pools.
Today I'll be focusing on drawing this behavior from Beeper v4 which I am rebranding as Beeper Micro - and expanding the structure using a new type experimental attention mechanism to replace traditional multihead attention dubbed GeometricCollectiveAttention.
This attention is similar to multihead attention, except it's considerably harder to burn at higher learn rates. This coupled with a new perspective on training pentachora into the LLM structure will allow a full relay structural system.
beeper-small will house a full rope - except not in the traditional vocabulary set. Beeper-small will not have a vocabulary.
beeper-small is my first non-linear non-Euclidean attempt to create a pure symbolic auto-completion LLM; which may be naiive according to many researchers who have tried similar systems historically.
I've personally analyzed many papers, many studies, and many techniques that have attempted similar non-vocabulary entropic learning, and I believe the pentachora lattice will hold with pure binary, not requiring a vocabulary.
Transformers really like vocabulary... beeper likes... geometry, and this experiment for beeper-small will have a new type of ROPE that is based entirely on vertices developed from the direct unicode represented characters, rather than a full vocabulary structure meant to bring solidity from chaos.
The first beeper experiment showed many insights into how similarity and internal classification responds mathematically with traditional ML techniques, and those techniques did not reject the construct - on the contrary. The control group placebo beeper, the traditional non-rose version BURNED under half lr. It's completely illegible, producing garbage and noise, while rose beeper sings
-
- 2 replies
AbstractPhil
posted
an
update
22 days ago
Post
490
After a multitude of notebooks and semi-successful experiments, I now have a series of hyperparameters semi-capable of tuning pentachoron simplex models tuned specifically with frequency and resonance.
AbstractPhil/pentachora-greyscale-frequency-encoded
AbstractPhil/pentachora-multi-channel-frequency-encoded
They are essentially geometric crystallization engines that store an excess amount of information in a very constrained and tight location - capable of classification *within a fraction of the size of traditional linear systems* along with the added benefit of only needing minimal tuning and learning at a very high learn rate - yielding a very complex structural response to complex learning.
I have 3 more notebooks to prep and release for the full pentachora classification structure based on the Nikola architecture concepts, fused with many rules that govern physics, laws of conservation, atomic structural comparators, and many more experiments that were interesting but yielded less than anticipated for some.
The most robust representation is a representational geometric collective, a series of geometric experts capable of high-yield classification with multiple ongoing simultaneous opinions.
The quick training capability of these crystals have shown that they can be rapidly trained and discarded as massive collectives, pruning based on comprehensive capability and combining working geometry with the survivors - enabling the accuracy to reach very high levels that were unattainable with standard ML learning gradient loss paradigms without reaching into the large model spectrum.
I've since begun integrating them into LLMS and will be releasing the notebooks as they are prepared, along with decomposition and comparative studies for the most comprehensive and capable training paradigms, as well as proof of concept for additional capabilities and the full araxiv paper triad when the studies conclude.
AbstractPhil/pentachora-greyscale-frequency-encoded
AbstractPhil/pentachora-multi-channel-frequency-encoded
They are essentially geometric crystallization engines that store an excess amount of information in a very constrained and tight location - capable of classification *within a fraction of the size of traditional linear systems* along with the added benefit of only needing minimal tuning and learning at a very high learn rate - yielding a very complex structural response to complex learning.
I have 3 more notebooks to prep and release for the full pentachora classification structure based on the Nikola architecture concepts, fused with many rules that govern physics, laws of conservation, atomic structural comparators, and many more experiments that were interesting but yielded less than anticipated for some.
The most robust representation is a representational geometric collective, a series of geometric experts capable of high-yield classification with multiple ongoing simultaneous opinions.
The quick training capability of these crystals have shown that they can be rapidly trained and discarded as massive collectives, pruning based on comprehensive capability and combining working geometry with the survivors - enabling the accuracy to reach very high levels that were unattainable with standard ML learning gradient loss paradigms without reaching into the large model spectrum.
I've since begun integrating them into LLMS and will be releasing the notebooks as they are prepared, along with decomposition and comparative studies for the most comprehensive and capable training paradigms, as well as proof of concept for additional capabilities and the full araxiv paper triad when the studies conclude.
ImranzamanML
posted
an
update
26 days ago
Post
418
# Runway Aleph: The Future of AI Video Editing
Runway’s new **Aleph** model lets you *transform*, *edit*, and *generate* video from existing footage using just text prompts.
You can remove objects, change environments, restyle shots, alter lighting, and even create entirely new camera angles, all in one tool.
## Key Links
- 🔬 [Introducing Aleph (Runway Research)](https://runwayml.com/research/introducing-runway-aleph)
- 📖 [Aleph Prompting Guide (Runway Help Center)](https://help.runwayml.com/hc/en-us/articles/43277392678803-Aleph-Prompting-Guide)
- 🎬 [How to Transform Videos (Runway Academy)](https://academy.runwayml.com/aleph/how-to-transform-videos)
- 📰 [Gadgets360 Coverage](https://www.gadgets360.com/ai/news/runway-aleph-ai-video-editing-generation-model-post-production-unveiled-8965180)
- 🎥 [YouTube Demo: ALEPH by Runway](https://www.youtube.com/watch?v=PPerCtyIKwA)
- 📰 [Runway Alpha dataset]( Rapidata/text-2-video-human-preferences-runway-alpha)
## Prompt Tips
1. Be clear and specific (e.g., _“Change to snowy night, keep people unchanged”_).
2. Use action verbs like _add, remove, restyle, relight_.
3. Add reference images for style or lighting.
Aleph shifts AI video from *text-to-video* to *video-to-video*, making post-production faster, more creative, and more accessible than ever.
Runway’s new **Aleph** model lets you *transform*, *edit*, and *generate* video from existing footage using just text prompts.
You can remove objects, change environments, restyle shots, alter lighting, and even create entirely new camera angles, all in one tool.
## Key Links
- 🔬 [Introducing Aleph (Runway Research)](https://runwayml.com/research/introducing-runway-aleph)
- 📖 [Aleph Prompting Guide (Runway Help Center)](https://help.runwayml.com/hc/en-us/articles/43277392678803-Aleph-Prompting-Guide)
- 🎬 [How to Transform Videos (Runway Academy)](https://academy.runwayml.com/aleph/how-to-transform-videos)
- 📰 [Gadgets360 Coverage](https://www.gadgets360.com/ai/news/runway-aleph-ai-video-editing-generation-model-post-production-unveiled-8965180)
- 🎥 [YouTube Demo: ALEPH by Runway](https://www.youtube.com/watch?v=PPerCtyIKwA)
- 📰 [Runway Alpha dataset]( Rapidata/text-2-video-human-preferences-runway-alpha)
## Prompt Tips
1. Be clear and specific (e.g., _“Change to snowy night, keep people unchanged”_).
2. Use action verbs like _add, remove, restyle, relight_.
3. Add reference images for style or lighting.
Aleph shifts AI video from *text-to-video* to *video-to-video*, making post-production faster, more creative, and more accessible than ever.
ImranzamanML
posted
an
update
about 1 month ago
Post
576
OpenAI has launched GPT-5, a significant leap forward in AI technology that is now available to all users. The new model unifies all of OpenAI's previous developments into a single, cohesive system that automatically adapts its approach based on the complexity of the user's request. This means it can prioritize speed for simple queries or engage a deeper reasoning model for more complex problems, all without the user having to manually switch settings.
Key Features and Improvements
Unified System: GPT-5 combines various models into one interface, intelligently selecting the best approach for each query.
Enhanced Coding: It's being hailed as the "strongest coding model to date," with the ability to create complex, responsive websites and applications from a single prompt.
PhD-level Reasoning: According to CEO Sam Altman, GPT-5 offers a significant jump in reasoning ability, with a much lower hallucination rate. It also performs better on academic and human-evaluated benchmarks.
New Personalities: Users can now select from four preset personalities—Cynic, Robot, Listener and Nerd to customize their chat experience.
Advanced Voice Mode: The voice mode has been improved to sound more natural and adapt its speech based on the context of the conversation.
https://openai.com/index/introducing-gpt-5/
https://openai.com/gpt-5/
Key Features and Improvements
Unified System: GPT-5 combines various models into one interface, intelligently selecting the best approach for each query.
Enhanced Coding: It's being hailed as the "strongest coding model to date," with the ability to create complex, responsive websites and applications from a single prompt.
PhD-level Reasoning: According to CEO Sam Altman, GPT-5 offers a significant jump in reasoning ability, with a much lower hallucination rate. It also performs better on academic and human-evaluated benchmarks.
New Personalities: Users can now select from four preset personalities—Cynic, Robot, Listener and Nerd to customize their chat experience.
Advanced Voice Mode: The voice mode has been improved to sound more natural and adapt its speech based on the context of the conversation.
https://openai.com/index/introducing-gpt-5/
https://openai.com/gpt-5/
ImranzamanML
posted
an
update
about 1 month ago
Post
286
All key links to OpenAI open sourced GPT OSS models (117B and 21B) which are released under apache 2.0. Here is a quick guide to explore and build with them:
Intro & vision: https://openai.com/index/introducing-gpt-oss
Model specs & license: https://openai.com/index/gpt-oss-model-card/
Dev overview: https://cookbook.openai.com/topic/gpt-oss
How to run via vLLM: https://cookbook.openai.com/articles/gpt-oss/run-vllm
Harmony I/O format: https://github.com/openai/harmony
Reference PyTorch code: https://github.com/openai/gpt-oss?tab=readme-ov-file#reference-pytorch-implementation
Community site: https://gpt-oss.com/
Lets deep dive with OpenAI models now 😊
#OpenSource #AI #GPTOSS #OpenAI #LLM #Python #GenAI
Intro & vision: https://openai.com/index/introducing-gpt-oss
Model specs & license: https://openai.com/index/gpt-oss-model-card/
Dev overview: https://cookbook.openai.com/topic/gpt-oss
How to run via vLLM: https://cookbook.openai.com/articles/gpt-oss/run-vllm
Harmony I/O format: https://github.com/openai/harmony
Reference PyTorch code: https://github.com/openai/gpt-oss?tab=readme-ov-file#reference-pytorch-implementation
Community site: https://gpt-oss.com/
Lets deep dive with OpenAI models now 😊
#OpenSource #AI #GPTOSS #OpenAI #LLM #Python #GenAI
wangsssssss
authored
a
paper
about 1 month ago
ImranzamanML
posted
an
update
about 1 month ago
Post
3512
Finaly OpenAI is open to share open-source models after GPT2-2019.
gpt-oss-120b
gpt-oss-20b
openai/gpt-oss-120b
#AI #GPT #LLM #Openai
gpt-oss-120b
gpt-oss-20b
openai/gpt-oss-120b
#AI #GPT #LLM #Openai
- 1 reply
ImranzamanML
posted
an
update
about 1 month ago
Post
310
Working of Transformer model layers!
I focused on showing the core steps side by side with tokenization, embedding and the transformer model layers, each highlighting the self attention and feedforward parts without getting lost in too much technical depth.
Its showing how these layers work together to understand context and generate meaningful output!
If you are curious about the architecture behind AI language models or want a clean way to explain it, hit me up, I’d love to share!
#AI #MachineLearning #NLP #Transformers #DeepLearning #DataScience #LLM #AIAgents
I focused on showing the core steps side by side with tokenization, embedding and the transformer model layers, each highlighting the self attention and feedforward parts without getting lost in too much technical depth.
Its showing how these layers work together to understand context and generate meaningful output!
If you are curious about the architecture behind AI language models or want a clean way to explain it, hit me up, I’d love to share!
#AI #MachineLearning #NLP #Transformers #DeepLearning #DataScience #LLM #AIAgents
ImranzamanML
posted
an
update
about 1 month ago
Post
1651
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
- 1 reply
ImranzamanML
authored
a
paper
about 2 months ago
Skylion007
authored
a
paper
3 months ago
wangsssssss
authored
a
paper
3 months ago