Command A: An Enterprise-Ready Large Language Model
Paper
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2504.00698
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Published
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17
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sanderland
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about 21 hours ago
MarziehFadaee
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about 21 hours ago
beyzaermis
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about 21 hours ago
johndang-cohere
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about 21 hours ago
hesamation
posted
an
update
1 day ago
Post
1800
What, How, Where, and How Well? This paper reviews test-time scaling methods and all you need to know about them:
> parallel, sequential, hybrid, internal scaling
> how to scale (SFT, RL, search, verification)
> metrics and evals of test-time scaling
🔗paper: What, How, Where, and How Well? A Survey on Test-Time Scaling in Large Language Models (2503.24235)
If you want to learn what inference-time compute scaling is @rasbt has a great blog post on that:
https://magazine.sebastianraschka.com/p/state-of-llm-reasoning-and-inference-scaling
> parallel, sequential, hybrid, internal scaling
> how to scale (SFT, RL, search, verification)
> metrics and evals of test-time scaling
🔗paper: What, How, Where, and How Well? A Survey on Test-Time Scaling in Large Language Models (2503.24235)
If you want to learn what inference-time compute scaling is @rasbt has a great blog post on that:
https://magazine.sebastianraschka.com/p/state-of-llm-reasoning-and-inference-scaling
hesamation
posted
an
update
3 days ago
Post
1667
this paper lists ways to make reasoning LLMs more efficient:
> enforce token limits per reasoning step
> route tasks to different models (small/large)
> compress reasoning chains during SFT
> reward based on reasoning length
> parallel search at test-time
and more...
@Xiaoye08 @yaful @Warrieryes
A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond (2503.21614)
> enforce token limits per reasoning step
> route tasks to different models (small/large)
> compress reasoning chains during SFT
> reward based on reasoning length
> parallel search at test-time
and more...
@Xiaoye08 @yaful @Warrieryes
A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond (2503.21614)
Post
1484
What inspired the Transformer architecture in the "Attention Is All You Need" paper? And how were various ideas combined to create this groundbreaking model?
In this lengthy article, I explore the story and the origins of some of the ideas introduced in the paper. We'll explore everything from the fundamental attention mechanism that lies at its heart to the surprisingly simple explanation for its name, Transformer.
💡 Examples of ideas explored in the article:
✅ What was the inspiration for the attention mechanism?
✅ How did we go from attention to self-attention?
✅ Did the team have any other names in mind for the model?
and more...
I aim to tell the story of Transformers as I would have wanted to read it, and hopefully, one that appeals to others interested in the details of this fascinating idea. This narrative draws from video interviews, lectures, articles, tweets/Xs, and some digging into the literature. I have done my best to be accurate, but errors are possible. If you find inaccuracies or have any additions, please do reach out, and I will gladly make the necessary updates.
Read the article: https://huggingface.co/blog/mmhamdy/pandemonium-the-transformers-story
In this lengthy article, I explore the story and the origins of some of the ideas introduced in the paper. We'll explore everything from the fundamental attention mechanism that lies at its heart to the surprisingly simple explanation for its name, Transformer.
💡 Examples of ideas explored in the article:
✅ What was the inspiration for the attention mechanism?
✅ How did we go from attention to self-attention?
✅ Did the team have any other names in mind for the model?
and more...
I aim to tell the story of Transformers as I would have wanted to read it, and hopefully, one that appeals to others interested in the details of this fascinating idea. This narrative draws from video interviews, lectures, articles, tweets/Xs, and some digging into the literature. I have done my best to be accurate, but errors are possible. If you find inaccuracies or have any additions, please do reach out, and I will gladly make the necessary updates.
Read the article: https://huggingface.co/blog/mmhamdy/pandemonium-the-transformers-story
Aurelien-Morgan
posted
an
update
5 days ago
takarajordan
posted
an
update
9 days ago
Post
1838
Takara takes 3rd place in the {tech:munich} AI hackathon with Fudeno!
A little over 2 weeks ago @aldigobbler and I set out to create the largest MultiModal SVG dataset ever created, we succeeded in this and when I was in Munich, Germany I took it one step further and made an entire app with it!
We fine-tuned Mistral Small, made a Next.JS application and blew some minds, taking 3rd place out of over 100 hackers. So cool!
If you want to see the dataset, please see below.
takara-ai/fudeno-instruct-4M
A little over 2 weeks ago @aldigobbler and I set out to create the largest MultiModal SVG dataset ever created, we succeeded in this and when I was in Munich, Germany I took it one step further and made an entire app with it!
We fine-tuned Mistral Small, made a Next.JS application and blew some minds, taking 3rd place out of over 100 hackers. So cool!
If you want to see the dataset, please see below.
takara-ai/fudeno-instruct-4M
louisbrulenaudet
posted
an
update
11 days ago
Post
826
I’ve just released logfire-callback on PyPI, designed to facilitate monitoring of Hugging Face Transformer training loops using Pydantic Logfire 🤗
The callback will automatically log training start with configuration parameters, periodic metrics and training completion ⏱️
Install the package using pip:
First, ensure you have a Logfire API token and set it as an environment variable:
Then use the callback in your training code:
If you have any feedback, please reach out at @louisbrulenaudet
The callback will automatically log training start with configuration parameters, periodic metrics and training completion ⏱️
Install the package using pip:
pip install logfire-callbackFirst, ensure you have a Logfire API token and set it as an environment variable:
export LOGFIRE_TOKEN=your_logfire_tokenThen use the callback in your training code:
from transformers import Trainer, TrainingArguments
from logfire_callback import LogfireCallback
# Initialize your model, dataset, etc.
training_args = TrainingArguments(
output_dir="./results",
num_train_epochs=3,
# ... other training arguments
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
callbacks=[LogfireCallback()] # Add the Logfire callback here
)
trainer.train()If you have any feedback, please reach out at @louisbrulenaudet
tellarin
authored
a
paper
16 days ago
AhmadMustafa
authored
a
paper
18 days ago
ljvmiranda921
authored
2
papers
22 days ago
Post
1737
🚀AraClip is now fully integrated with Hugging Face 🤗
AraClip is a specialized CLIP model that was created by @pain and optimized for Arabic text-image retrieval tasks🔥
🔗 Try it out 🔗
🤖 model: Arabic-Clip/araclip
🧩 Gradio demo: Arabic-Clip/Araclip-Simplified
🌐 website: https://arabic-clip.github.io/Arabic-CLIP/
AraClip is a specialized CLIP model that was created by @pain and optimized for Arabic text-image retrieval tasks🔥
🔗 Try it out 🔗
🤖 model: Arabic-Clip/araclip
🧩 Gradio demo: Arabic-Clip/Araclip-Simplified
🌐 website: https://arabic-clip.github.io/Arabic-CLIP/
- 2 replies
tellarin
authored
a
paper
22 days ago
tellarin
authored
3
papers
23 days ago
INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge
Paper
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2411.19799
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Published
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14
Multi-Level Knowledge Distillation for Out-of-Distribution Detection in Text
Paper
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2211.11300
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Published
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1
Taking Notes Brings Focus? Towards Multi-Turn Multimodal Dialogue Learning
Paper
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2503.07002
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Published
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38
jjzha
authored
a
paper
24 days ago