Tensor Diffusion

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AI & ML interests

StableDIffusion, Computer Vision, NLP

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tensor-diffusion's activity

Nymbo 
posted an update 4 days ago
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1091
Haven't seen this posted anywhere - Llama-3.3-8B-Instruct is available on the new Llama API. Is this a new model or did someone mislabel Llama-3.1-8B?
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Nymbo 
posted an update 12 days ago
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1063
PSA for anyone using Nymbo/Nymbo_Theme or Nymbo/Nymbo_Theme_5 in a Gradio space ~

Both of these themes have been updated to fix some of the long-standing inconsistencies ever since the transition to Gradio v5. Textboxes are no longer bright green and in-line code is readable now! Both themes are now visually identical across versions.

If your space is already using one of these themes, you just need to restart your space to get the latest version. No code changes needed.
not-lain 
posted an update 2 months ago
DamarJati 
updated a Space 3 months ago
not-lain 
posted an update 3 months ago
not-lain 
posted an update 4 months ago
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1708
we now have more than 2000 public AI models using ModelHubMixin🤗
not-lain 
posted an update 4 months ago
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4070
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 :
DamarJati 
posted an update 4 months ago
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3555
Happy New Year 2025 🤗
For the Huggingface community.
1aurent 
posted an update 4 months ago
not-lain 
posted an update 6 months ago
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2379
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="")