deepghs/yolo-face
Object Detection
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DeepGHS
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Computer Vision Technology and Data Collection for Anime Waifu
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narugoΒ
updated
2
models
43 minutes ago
Post
1346
I have just released a new blogpost about kv caching and its role in inference speedup π
π https://huggingface.co/blog/not-lain/kv-caching/
some takeaways :
π https://huggingface.co/blog/not-lain/kv-caching/
some takeaways :
- 4 replies
Post
2113
ππ»ββοΈ 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 β€οΈ like basically to vote for us !
Mistral-AI-Game-Jam/TextToSurvive
hope you like it !
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 β€οΈ like basically to vote for us !
Mistral-AI-Game-Jam/TextToSurvive
hope you like it !
Delta-VectorΒ
posted
an
update
4 days ago
Post
559
For anyone that enjoys Magnum models, I just dropped a 12B that is the first (or second?) stepping stone into Magnum V5
Delta-Vector/rei-12b-6795505005c4a94ebdfdeb39
Delta-Vector/rei-12b-6795505005c4a94ebdfdeb39
Post
1193
we now have more than 2000 public AI models using ModelHubMixinπ€
Post
1506
ππ»ββοΈ 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
Facebook AI just released JASCO models that make music stems .
you can try it out here : Tonic/audiocraft
hope you like it
Post
2402
ππ»ββοΈHey 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 π
Post
3930
Published a new blogpost π
In this blogpost I have gone through the transformers' architecture emphasizing how shapes propagate throughout each layer.
π https://huggingface.co/blog/not-lain/tensor-dims
some interesting takeaways :
In this blogpost I have gone through the transformers' architecture emphasizing how shapes propagate throughout each layer.
π https://huggingface.co/blog/not-lain/tensor-dims
some interesting takeaways :
Skylion007Β
authored
a
paper
21 days ago
Post
2544
Happy New Year 2025 π€
For the Huggingface community.
For the Huggingface community.
eienmojikiΒ
posted
an
update
about 2 months ago
Post
1496
π Introducing 2048 Game API: A RESTful API for the Classic Puzzle Game π§©
I'm excited to share my latest project, 2048 Game API, a RESTful API that allows you to create, manage, and play games of 2048, a popular puzzle game where players slide numbered tiles to combine them and reach the goal of getting a tile with the value of 2048.
β Features
Create new games with customizable board sizes (3-8)
Make moves (up, down, left, right) and get the updated game state
Get the current game state, including the board, score, and game over status
Delete games
Generate images of the game board with customizable themes (light and dark)
π API Endpoints
𧩠Example Use Cases
- Create a new game with a 4x4 board:
- Make a move up:
- Get the current game state:
π Try it out!
- Demo: eienmojiki/2048
- Source: https://github.com/kogakisaki/koga-2048
- You can try out the API by running the server locally or using a tool like Postman to send requests to the API. I hope you enjoy playing 2048 with this API!
Let me know if you have any questions or feedback!
π§ Mouse1 is our friendπ§
I'm excited to share my latest project, 2048 Game API, a RESTful API that allows you to create, manage, and play games of 2048, a popular puzzle game where players slide numbered tiles to combine them and reach the goal of getting a tile with the value of 2048.
β Features
Create new games with customizable board sizes (3-8)
Make moves (up, down, left, right) and get the updated game state
Get the current game state, including the board, score, and game over status
Delete games
Generate images of the game board with customizable themes (light and dark)
π API Endpoints
POST /api/games - Create a new gameGET /api/games/:gameId - Get the current game statePOST /api/games/:gameId/move - Make a move (up, down, left, right)DELETE /api/games/:gameId - Delete a gameGET /api/games/:gameId/image - Generate an image of the game board𧩠Example Use Cases
- Create a new game with a 4x4 board:
curl -X POST -H "Content-Type: application/json" -d '{"size": 4}' http://localhost:3000/api/games- Make a move up:
curl -X POST -H "Content-Type: application/json" -d '{"direction": "up"}' http://localhost:3000/api/games/:gameId/move- Get the current game state:
curl -X GET http://localhost:3000/api/games/:gameIdπ Try it out!
- Demo: eienmojiki/2048
- Source: https://github.com/kogakisaki/koga-2048
- You can try out the API by running the server locally or using a tool like Postman to send requests to the API. I hope you enjoy playing 2048 with this API!
Let me know if you have any questions or feedback!
π§ Mouse1 is our friendπ§
ImranzamanMLΒ
posted
an
update
about 2 months ago
Post
559
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 β Concordant β
(P1, P3): P3 has a longer survival time and a lower risk score β Concordant β
(P2, P3): P3 has a longer survival time and a lower risk score β Concordant β
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).
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 β Concordant β
(P1, P3): P3 has a longer survival time and a lower risk score β Concordant β
(P2, P3): P3 has a longer survival time and a lower risk score β Concordant β
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).
- 1 reply
Post
1679
KBlueLeafΒ
authored
a
paper
2 months ago
Post
2294
ever wondered how you can make an API call to a visual-question-answering model without sending an image url π
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.
π https://github.com/not-lain/loadimg
API request example π οΈ:
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.
π https://github.com/not-lain/loadimg
API request example π οΈ:
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="")Post
3561
ππ»ββοΈhey there folks,
periodic reminder : if you are experiencing β οΈ500 errors β οΈ or β οΈ abnormal
we have a thread ππ» 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 π€π€π€
periodic reminder : if you are experiencing β οΈ500 errors β οΈ or β οΈ abnormal
spaces behavior on load or launch β οΈwe have a thread ππ» 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 π€π€π€
- 2 replies
Post
1168
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.
-
- 1 reply
ImranzamanMLΒ
posted
an
update
3 months ago
Post
716
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 :)
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 :)