Llama 3.2
Collection
This collection hosts the transformers and original repos of the Llama 3.2 and Llama Guard 3
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15 items
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Joseph
Joseph717171
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Llama 3.2
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meta-llama/Llama-3.2-3B-Instruct-SpinQuant_INT4_EO8
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Joseph717171's activity
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Reacted to
fdaudens's
post with 👍👀
2 days ago
Post
1242
🤖 93% of Gen Z workers use AI tools weekly, but nearly half of all workers aren't comfortable admitting it. The tech adoption gap isn't about usage—it's about openness. Why are we still treating AI tools like a workplace secret? 🤔
See this article: https://www.axios.com/2024/11/25/gen-z-ai-work-survey
See this article: https://www.axios.com/2024/11/25/gen-z-ai-work-survey
- 1 reply
Reacted to
loubnabnl's
post with 🤗🔥
2 days ago
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1236
Making SmolLM2 reproducible: open-sourcing our training & evaluation toolkit 🛠️ https://github.com/huggingface/smollm/
- Pre-training code with nanotron
- Evaluation suite with lighteval
- Synthetic data generation using distilabel (powers our new SFT dataset HuggingFaceTB/smoltalk)
- Post-training scripts with TRL & the alignment handbook
- On-device tools with llama.cpp for summarization, rewriting & agents
Apache 2.0 licensed. V2 pre-training data mix coming soon!
Which other tools should we add next?
- Pre-training code with nanotron
- Evaluation suite with lighteval
- Synthetic data generation using distilabel (powers our new SFT dataset HuggingFaceTB/smoltalk)
- Post-training scripts with TRL & the alignment handbook
- On-device tools with llama.cpp for summarization, rewriting & agents
Apache 2.0 licensed. V2 pre-training data mix coming soon!
Which other tools should we add next?
liked
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models
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Reacted to
TuringsSolutions's
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5 days ago
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800
I created something called 'Hyperbolic Embeddings'. I literally just embed the tokens into Hyperbolic Space instead of Euclidean space. At first, this did not get me the gains I was expecting. I was a sad panda. Then I thought about it, a Hyperbolic Embedding needs a Hyperbolic Optimizer. So, instead of Adam, I used Riemannian Adam (RAdam). "Ladies and Gentlemen, We Got 'Em!"
- 27 replies
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davidberenstein1957's
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5 days ago
Post
1014
🤗🔭 Introducing Observers: A Lightweight SDK for AI Observability 🔭🤗
Observers is an open-source Python SDK that provides comprehensive observability for AI applications. Our library makes it easy to:
- Track and record interactions with AI models
- Store observations in multiple backends
- Query and analyse your AI interactions with ease
https://huggingface.co/blog/davidberenstein1957/observers-a-lightweight-sdk-for-ai-observability
Observers is an open-source Python SDK that provides comprehensive observability for AI applications. Our library makes it easy to:
- Track and record interactions with AI models
- Store observations in multiple backends
- Query and analyse your AI interactions with ease
https://huggingface.co/blog/davidberenstein1957/observers-a-lightweight-sdk-for-ai-observability
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cfahlgren1's
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7 days ago
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838
You can create charts, leaderboards, and filters on top of any Hugging Face dataset in less than a minute
• ASCII Bar Charts 📊
• Powered by DuckDB WASM ⚡
• Download results to Parquet 💽
• Embed and Share results with friends 📬
Do you have any interesting queries?
• ASCII Bar Charts 📊
• Powered by DuckDB WASM ⚡
• Download results to Parquet 💽
• Embed and Share results with friends 📬
Do you have any interesting queries?
Update SuperNova-Medius with a merge with Qwen/Qwen2.5-Coder-14B-Instruct + Further Training 😋
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#12 opened 17 days ago
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Joseph717171
Joseph717171
Reacted to
jsulz's
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7 days ago
Post
2854
When the XetHub crew joined Hugging Face this fall,
@erinys
and I started brainstorming how to share our work to replace Git LFS on the Hub. Uploading and downloading large models and datasets takes precious time. That’s where our chunk-based approach comes in.
Instead of versioning files (like Git and Git LFS), we version variable-sized chunks of data. For the Hugging Face community, this means:
⏩ Only upload the chunks that changed.
🚀 Download just the updates, not the whole file.
🧠 We store your file as deduplicated chunks
In our benchmarks, we found that using CDC to store iterative model and dataset version led to transfer speedups of ~2x, but this isn’t just a performance boost. It’s a rethinking of how we manage models and datasets on the Hub.
We're planning on our new storage backend to the Hub in early 2025 - check out our blog to dive deeper, and let us know: how could this improve your workflows?
https://huggingface.co/blog/from-files-to-chunks
Instead of versioning files (like Git and Git LFS), we version variable-sized chunks of data. For the Hugging Face community, this means:
⏩ Only upload the chunks that changed.
🚀 Download just the updates, not the whole file.
🧠 We store your file as deduplicated chunks
In our benchmarks, we found that using CDC to store iterative model and dataset version led to transfer speedups of ~2x, but this isn’t just a performance boost. It’s a rethinking of how we manage models and datasets on the Hub.
We're planning on our new storage backend to the Hub in early 2025 - check out our blog to dive deeper, and let us know: how could this improve your workflows?
https://huggingface.co/blog/from-files-to-chunks