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Jofthomas 
posted an update about 2 months ago
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3457
Meet our new agentic model : 𝗗𝗲𝘃𝘀𝘁𝗿𝗮𝗹

Devstral is an open-source LLM built software engineering tasks built under a collaboration between Mistral AI and All Hands AI 🙌.

𝗞𝗲𝘆 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 :
• 🤖 𝗔𝗴𝗲𝗻𝘁𝘀 : perfect for Agentic coding
• 🍃 𝗹𝗶𝗴𝗵𝘁𝘄𝗲𝗶𝗴𝗵𝘁: Devstral is a 𝟮𝟰𝗕 parameter based on Mistral small.
• ©️ 𝗔𝗽𝗮𝗰𝗵𝗲 𝟮.𝟬, meaning fully open-source !
• 📄 A 𝟭𝟮𝟴𝗸 context window.

📚Blog : https://mistral.ai/news/devstral
⚡API : The model is also available on our API under the name 𝗱𝗲𝘃𝘀𝘁𝗿𝗮𝗹-𝘀𝗺𝗮𝗹𝗹-𝟮𝟱𝟬𝟱
🤗 repo : mistralai/Devstral-Small-2505

Can't wait to see what you will build with it !
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not-lain 
posted an update 4 months ago
not-lain 
posted an update 5 months ago
not-lain 
posted an update 6 months ago
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1784
we now have more than 2000 public AI models using ModelHubMixin🤗
not-lain 
posted an update 6 months ago
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4131
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 :
not-lain 
posted an update 8 months ago
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2437
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="")
Jofthomas 
posted an update 11 months ago
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7641
Everchanging Quest is out !

It is an LLM controlled Rogue-Like in which the LLM gets a markdown representation of the map, and should generate a JSON with the objective to fulfill on the map as well as the necessary objects and their placements.

Come test it on the space :
Jofthomas/Everchanging-Quest
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not-lain 
posted an update 11 months ago
not-lain 
posted an update 12 months ago
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7811
I am now a huggingface fellow 🥳
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