We’re open-sourcing our text-to-image model and the process behind it
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llmsinference-providersscaleway
Scaleway on Hugging Face Inference Providers 🔥
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September 19, 2025
llmsinference-providerspublic-ai
Public AI on Hugging Face Inference Providers 🔥
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September 17, 2025
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Groq on Hugging Face Inference Providers 🔥
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June 16, 2025
llmsinference-providersfeatherless
Featherless AI on Hugging Face Inference Providers 🔥
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June 12, 2025
llmsinference-providerscohere
Cohere on Hugging Face Inference Providers 🔥
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April 16, 2025
announcementhubinference-providers
Introducing Three New Serverless Inference Providers: Hyperbolic, Nebius AI Studio, and Novita 🔥
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101
February 18, 2025
announcementhubinference-providers
Welcome Fireworks.ai on the Hub 🎆
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February 14, 2025
announcementhubinference-providers
Welcome to Inference Providers on the Hub 🔥
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490
January 28, 2025
We’re open-sourcing our text-to-image model and the process behind it
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59
Text-to-image Architectural Experiments
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29
Projected Abliteration
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23
Uncensor any LLM with abliteration
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721
The 1 Billion Token Challenge: Finding the Perfect Pre-training Mix
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42
ViDoRe V3: a comprehensive evaluation of retrieval for enterprise use-cases
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46
The Heterogeneous Feature of RoPE-based Attention in Long-Context LLMs
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10
KV Caching Explained: Optimizing Transformer Inference Efficiency
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173
🌳 QAT: The Art of Growing a Bonsai Model
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14
AI Model Optimization More Flexible Than Ever
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8
Why Did MiniMax M2 End Up as a Full Attention Model?
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63
Granite 4.0 Nano: Just how small can you go?
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117
To Think or Not to Think: A Router for Hybrid LLMs
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6
PEFT: Parameter-Efficient Fine-Tuning Methods for LLMs
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49
Visualizing How VLMs Work
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44
On the Shifting Global Compute Landscape
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50
⛳ Optimizer: What Does It Do and Why We Need It
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5
Let's talk about LLM evaluation
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203
Making LLMs Smaller Without Breaking Them: A GLU-Aware Pruning Approach
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15
Fine-Tuning Your First Large Language Model (LLM) with PyTorch and Hugging Face
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86