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asigalov61 
posted an update about 1 month ago
Nymbo 
posted an update about 1 month ago
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2350
Anyone know how to reset Claude web's MCP config? I connected mine when the HF MCP first released with just the default example spaces added. I added lots of other MCP spaces but Claude.ai doesn't update the available tools... "Disconnecting" the HF integration does nothing, deleting it and adding it again does nothing.

Refreshing tools works fine in VS Code because I can manually restart it in mcp.json, but claude.ai has no such option. Anyone got any ideas?
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asigalov61 
posted an update about 1 month ago
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462
Hey guys!

I wanted to invite all of you who are interested in symbolic music AI to check out my Orpheus Music Transformer

IMHO the model turned out very well and it plays very well too.

I would really appreciate any feedback and likes. It helps a lot.

Here are the links for your convenience:

1) Orpheus Music Transformer main demo space asigalov61/Orpheus-Music-Transformer

2) Orpheus Music Transformer Collection asigalov61/orpheus-music-transformer-685c3c8e59ed1414c02bb8cd

3) Orpheus Music Transformer Models Repo asigalov61/Orpheus-Music-Transformer

I hope you will enjoy it :)

Sincerely,

Alex
Felguk 
posted an update 3 months ago
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2143
Where gone streamlit in huggingface?
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Nymbo 
posted an update 3 months ago
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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?
  • 1 reply
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Nymbo 
posted an update 3 months ago
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2752
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 5 months ago
not-lain 
posted an update 6 months ago
not-lain 
posted an update 7 months ago
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1790
we now have more than 2000 public AI models using ModelHubMixin🤗
not-lain 
posted an update 7 months ago
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4136
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 :
Sri-Vigneshwar-DJ 
posted an update 7 months ago
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835
Checkout phi-4 from Microsoft, dropped a day ago... If you ❤️ the Phi series, then here is the GGUF - Sri-Vigneshwar-DJ/phi-4-GGUF. phi-4 is a 14B highly efficient open LLM that beats much larger models at math and reasoning - check out evaluations on the Open LLM.

Technical paper - https://arxiv.org/pdf/2412.08905 ; The Data Synthesis approach is interesting
Sri-Vigneshwar-DJ 
posted an update 7 months ago
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2104
Just sharing a thought: I started using DeepSeek V3 a lot, and an idea struck me about agents "orchestrating during inference" on a test-time compute model like DeepSeek V3 or the O1 series.

Agents (Instruction + Function Calls + Memory) execute during inference, and based on the output decision, a decision is made to scale the time to reason or perform other tasks.
Sri-Vigneshwar-DJ 
posted an update 7 months ago
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2364
Combining smolagents with Anthropic’s best practices simplifies building powerful AI agents:

1. Code-Based Agents: Write actions as Python code, reducing steps by 30%.
2. Prompt Chaining: Break tasks into sequential subtasks with validation gates.
3. Routing: Classify inputs and direct them to specialized handlers.
4. Fallback: Handle tasks even if classification fails.

https://huggingface.co/blog/Sri-Vigneshwar-DJ/building-effective-agents-with-anthropics-best-pra
not-lain 
posted an update 9 months ago
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2449
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="")