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eienmojiki
posted
an
update
about 1 month ago
Post
2102
🪄 LayerDiffuse - Flux Version (Demo) 🪄
LayerDiffuse - Transparent Image Layer Diffusion using Latent Transparency
Demo: https://huggingface.co/spaces/eienmojiki/Flux-LayerDiffuse
LayerDiffuse - Transparent Image Layer Diffusion using Latent Transparency
Demo: https://huggingface.co/spaces/eienmojiki/Flux-LayerDiffuse

lucifertrj
posted
an
update
about 1 month ago
Post
541
Bhagavad Gita GPT assistant - Build fast RAG pipeline to index 1000+ pages using Binary Optimization
DeepSeek R-1 and Qdrant Binary Quantization
Check out the latest tutorial where we build a Bhagavad Gita GPT assistant—covering:
- DeepSeek R1 vs OpenAI O1
- Using Qdrant client with Binary Quantization
- Building the RAG pipeline with LlamaIndex
- Running inference with DeepSeek R1 Distill model on Groq
- Develop Streamlit app for the chatbot inference
Watch the full implementation here: https://www.youtube.com/watch?v=NK1wp3YVY4Q
DeepSeek R-1 and Qdrant Binary Quantization
Check out the latest tutorial where we build a Bhagavad Gita GPT assistant—covering:
- DeepSeek R1 vs OpenAI O1
- Using Qdrant client with Binary Quantization
- Building the RAG pipeline with LlamaIndex
- Running inference with DeepSeek R1 Distill model on Groq
- Develop Streamlit app for the chatbot inference
Watch the full implementation here: https://www.youtube.com/watch?v=NK1wp3YVY4Q
Post
4285
I have just released a new blogpost about kv caching and its role in inference speedup 🚀
🔗 https://huggingface.co/blog/not-lain/kv-caching/
some takeaways :
🔗 https://huggingface.co/blog/not-lain/kv-caching/
some takeaways :
Post
1649
we now have more than 2000 public AI models using ModelHubMixin🤗
Post
4022
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 :
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 :
nielsgl
updated
a
Space
2 months ago

asusevski
authored
a
paper
3 months ago

lucifertrj
posted
an
update
3 months ago
Post
540
Image Prompt Engineering Guide:
➡️ Artistic styling for Image generation
➡️ Prompt weighting using the parentheses method to generate realistic images.
➡️ Advanced features like style and positioning control[experimental].
➡️ Image placement on the generated AI image using Recraft V3 Mockup.
Watch: https://www.youtube.com/watch?v=d3nUG28-jIc
➡️ Artistic styling for Image generation
➡️ Prompt weighting using the parentheses method to generate realistic images.
➡️ Advanced features like style and positioning control[experimental].
➡️ Image placement on the generated AI image using Recraft V3 Mockup.
Watch: https://www.youtube.com/watch?v=d3nUG28-jIc
Post
1826
Post
2323
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 🛠️:
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="")

lucifertrj
authored
a
paper
5 months ago

lucifertrj
posted
an
update
5 months ago
Post
1551
AI Agents LlamaIndex in 40 minutes
The video covers code and workflow explanations for:
- Function Calling
- Function Calling Agents + Agent Runner
- Agentic RAG
- REAcT Agent: Build your own Search Assistant Agent
Watch: https://youtu.be/bHn4dLJYIqE
The video covers code and workflow explanations for:
- Function Calling
- Function Calling Agents + Agent Runner
- Agentic RAG
- REAcT Agent: Build your own Search Assistant Agent
Watch: https://youtu.be/bHn4dLJYIqE

bharat-raghunathan
updated
2
models
6 months ago
Post
4388
I am training a controlnet model for Flux. And some of my experiences:
Checkpoint-10000:
https://x.com/kadirnar_ai/status/1829831750471606668
Checkpoint-12000:
https://x.com/kadirnar_ai/status/1829889524962640001
Checkpoint-14000:
https://x.com/kadirnar_ai/status/1829989622878744711
Checkpoint (16000-18000):
https://x.com/kadirnar_ai/status/1830179551407665654
Dataset: kadirnar/fluxdev_controlnet_16k
GPU: 1xA100(80GB)
GPU Hours: 65
Checkpoint-10000:
https://x.com/kadirnar_ai/status/1829831750471606668
Checkpoint-12000:
https://x.com/kadirnar_ai/status/1829889524962640001
Checkpoint-14000:
https://x.com/kadirnar_ai/status/1829989622878744711
Checkpoint (16000-18000):
https://x.com/kadirnar_ai/status/1830179551407665654
Dataset: kadirnar/fluxdev_controlnet_16k
GPU: 1xA100(80GB)
GPU Hours: 65
tgohblio
updated
a
model
7 months ago
Post
1367
@Blane187
could you please modify the title of your blogpost? content is cool, title could be nicer imo https://huggingface.co/blog/Blane187/wtf-is-rvc
Post
6870
🔥 New state of the art model for background removal is out
🤗 You can try the model at ZhengPeng7/BiRefNet
📈 model shows impressive results outperforming briaai/RMBG-1.4
🚀 you can try out the model in: ZhengPeng7/BiRefNet_demo
📃paper: Bilateral Reference for High-Resolution Dichotomous Image Segmentation (2401.03407)
🤗 You can try the model at ZhengPeng7/BiRefNet
📈 model shows impressive results outperforming briaai/RMBG-1.4
🚀 you can try out the model in: ZhengPeng7/BiRefNet_demo
📃paper: Bilateral Reference for High-Resolution Dichotomous Image Segmentation (2401.03407)