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Computer Vision Technology and Data Collection for Anime Waifu

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ImranzamanML 
posted an update 1 day ago
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1674
Hugging Face just launched the AI Agents Course – a free journey from beginner to expert in AI agents!

- Learn AI Agent fundamentals, use cases and frameworks
- Use top libraries like LangChain & LlamaIndex
- Compete in challenges & earn a certificate
- Hands-on projects & real-world applications

https://huggingface.co/learn/agents-course/unit0/introduction

You can join for a live Q&A on Feb 12 at 5PM CET to learn more about the course here

https://www.youtube.com/live/PopqUt3MGyQ
eienmojiki 
posted an update 4 days ago
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1933
🪄 LayerDiffuse - Flux Version (Demo) 🪄

LayerDiffuse - Transparent Image Layer Diffusion using Latent Transparency

Demo: eienmojiki/Flux-LayerDiffuse
Tonic 
posted an update 8 days ago
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1985
🙋🏻‍♂️hey there folks ,

Goedel's Theorem Prover is now being demo'ed on huggingface : Tonic/Math

give it a try !
not-lain 
posted an update 13 days ago
Tonic 
posted an update 15 days ago
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2866
🙋🏻‍♂️ Hey there folks ,

our team made a game during the @mistral-game-jam and we're trying to win the community award !

try our game out and drop us a ❤️ like basically to vote for us !

Mistral-AI-Game-Jam/TextToSurvive

hope you like it !
not-lain 
posted an update 26 days ago
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we now have more than 2000 public AI models using ModelHubMixin🤗
Tonic 
posted an update 27 days ago
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🙋🏻‍♂️ Hey there folks ,

Facebook AI just released JASCO models that make music stems .

you can try it out here : Tonic/audiocraft

hope you like it
Tonic 
posted an update 29 days ago
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🙋🏻‍♂️Hey there folks , Open LLM Europe just released Lucie 7B-Instruct model , a billingual instruct model trained on open data ! You can check out my unofficial demo here while we wait for the official inference api from the group : Tonic/Lucie-7B hope you like it 🚀
not-lain 
posted an update about 1 month ago
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3989
Published a new blogpost 📖
In this blogpost I have gone through the transformers' architecture emphasizing how shapes propagate throughout each layer.
🔗 https://huggingface.co/blog/not-lain/tensor-dims
some interesting takeaways :
Tonic 
posted an update about 1 month ago
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microsoft just released Phi-4 , check it out here : Tonic/Phi-4

hope you like it :-)
DamarJati 
posted an update about 1 month ago
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2722
Happy New Year 2025 🤗
For the Huggingface community.
ImranzamanML 
posted an update 2 months ago
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610
Deep understanding of (C-index) evaluation measure for better model
Lets start with three patients groups:

Group A
Group B
Group C
For each patient, we will predict risk score (higher score means higher risk of early event).

Step 1: Understanding Concordance Index
The Concordance Index (C-index) evaluate that how well the model ranks survival times.

Understand with sample data:
Group A has 3 patients with actual survival times and predicted risk scores:

Patient Actual Survival Time Predicted Risk Score
P1 5 months 0.8
P2 3 months 0.9
P3 10 months 0.2
Comparable pairs:

(P1, P2): P2 has a shorter survival time and a higher risk score → Concordant ✅
(P1, P3): P3 has a longer survival time and a lower risk score → Concordant ✅
(P2, P3): P3 has a longer survival time and a lower risk score → Concordant ✅
Total pairs = 3
Total concordant pairs = 3

C-index for Group A = Concordant pairs/Total pairs= 3/3 = 1.0

Step 2: Calculate C-index for All Groups
Repeat the process for all groups. For now we can assume:

Group A: C-index = 1.0
Group B: C-index = 0.8
Group C: C-index = 0.6
Step 3: Stratified Concordance Index
The Stratified Concordance Index combines the C-index scores of all groups and focusing on the following:

Average performance across groups (mean of C-indices).
Consistency across groups (low standard deviation of C-indices).
Formula:
Stratified C-index = Mean(C-index scores) - Standard Deviation(C-index scores)

Calculate the mean:
Mean=1.0 + 0.8 + 0.6/3 = 0.8

Calculate the standard deviation:
Standard Deviation= sqrt((1.0-0.8)^2 + (0.8-0.8)^2 + (0.6-0.8)^/3) = 0.16

Stratified C-index:
Stratified C-index = 0.8 - 0.16 = 0.64

Step 4: Interpret the Results
A high Stratified C-index means:

The model predicts well overall (high mean C-index).
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lunarflu 
posted an update 2 months ago
not-lain 
posted an update 3 months ago
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2312
ever wondered how you can make an API call to a visual-question-answering model without sending an image url 👀

you can do that by converting your local image to base64 and sending it to the API.

recently I made some changes to my library "loadimg" that allows you to make converting images to base64 a breeze.
🔗 https://github.com/not-lain/loadimg

API request example 🛠️:
from loadimg import load_img
from huggingface_hub import InferenceClient

# or load a local image
my_b64_img = load_img(imgPath_url_pillow_or_numpy ,output_type="base64" ) 

client = InferenceClient(api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx")

messages = [
	{
		"role": "user",
		"content": [
			{
				"type": "text",
				"text": "Describe this image in one sentence."
			},
			{
				"type": "image_url",
				"image_url": {
					"url": my_b64_img # base64 allows using images without uploading them to the web
				}
			}
		]
	}
]

stream = client.chat.completions.create(
    model="meta-llama/Llama-3.2-11B-Vision-Instruct", 
	messages=messages, 
	max_tokens=500,
	stream=True
)

for chunk in stream:
    print(chunk.choices[0].delta.content, end="")
Tonic 
posted an update 3 months ago
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🙋🏻‍♂️hey there folks,

periodic reminder : if you are experiencing ⚠️500 errors ⚠️ or ⚠️ abnormal spaces behavior on load or launch ⚠️

we have a thread 👉🏻 https://discord.com/channels/879548962464493619/1295847667515129877

if you can record the problem and share it there , or on the forums in your own post , please dont be shy because i'm not sure but i do think it helps 🤗🤗🤗
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Tonic 
posted an update 4 months ago
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1177
boomers still pick zenodo.org instead of huggingface ??? absolutely clownish nonsense , my random datasets have 30x more downloads and views than front page zenodos ... gonna write a comparison blog , but yeah... cringe.
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ImranzamanML 
posted an update 4 months ago
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725
Easy steps for an effective RAG pipeline with LLM models!
1. Document Embedding & Indexing
We can start with the use of embedding models to vectorize documents, store them in vector databases (Elasticsearch, Pinecone, Weaviate) for efficient retrieval.

2. Smart Querying
Then we can generate query embeddings, retrieve top-K relevant chunks and can apply hybrid search if needed for better precision.

3. Context Management
We can concatenate retrieved chunks, optimize chunk order and keep within token limits to preserve response coherence.

4. Prompt Engineering
Then we can instruct the LLM to leverage retrieved context, using clear instructions to prioritize the provided information.

5. Post-Processing
Finally we can implement response verification, fact-checking and integrate feedback loops to refine the responses.

Happy to connect :)
Tonic 
posted an update 4 months ago
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🙋🏻‍♂️ hey there folks ,

really enjoying sharing cool genomics and protein datasets on the hub these days , check out our cool new org : https://huggingface.co/seq-to-pheno

scroll down for the datasets, still figuring out how to optimize for discoverability , i do think on that part it will be better than zenodo[dot}org , it would be nice to write a tutorial about that and compare : we already have more downloads than most zenodo datasets from famous researchers !