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Erik Kaunismäki
erikkaum
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Add feature-extraction as pipline tag
#3 opened 13 days ago
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erikkaum
Add feature-extraction as pipline tag
#6 opened 13 days ago
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erikkaum
Add feature-extraction as pipline tag
#16 opened 13 days ago
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erikkaum
WTF is going on?
11
#71 opened 6 months ago
by
vbarrier
commented on
Test-Driving the LLMD Inference Engine by ZML 🚀
3 months ago
posted
an
update
3 months ago
Post
2593
ZML just released a technical preview of their new Inference Engine: LLMD.
- Just 2.4GB container, which means fast startup times and efficient autoscaling
- Cross-Platform GPU Support: works on both NVIDIA and AMD GPUs.
- written in Zig
I just tried it out and deployed it on Hugging Face Inference Endpoints and wrote a quick guide 👇 You can try it in like 5 minutes!
https://huggingface.co/blog/erikkaum/test-driving-llmd-inference-engine
- Just 2.4GB container, which means fast startup times and efficient autoscaling
- Cross-Platform GPU Support: works on both NVIDIA and AMD GPUs.
- written in Zig
I just tried it out and deployed it on Hugging Face Inference Endpoints and wrote a quick guide 👇 You can try it in like 5 minutes!
https://huggingface.co/blog/erikkaum/test-driving-llmd-inference-engine
- 1 reply
published
an
article
3 months ago
Article
Test-Driving the LLMD Inference Engine by ZML 🚀
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posted
an
update
3 months ago
Post
2100
We just released native support for
@SGLang
and
@vllm-project
in Inference Endpoints 🔥
Inference Endpoints is becoming the central place where you deploy high performance Inference Engines.
And that provides the managed infra for it. Instead of spending weeks configuring infrastructure, managing servers, and debugging deployment issues, you can focus on what matters most: your AI model and your users 🙌
Inference Endpoints is becoming the central place where you deploy high performance Inference Engines.
And that provides the managed infra for it. Instead of spending weeks configuring infrastructure, managing servers, and debugging deployment issues, you can focus on what matters most: your AI model and your users 🙌
upvoted
an
article
4 months ago
Article
Nano-vLLM meets Inference Endpoints
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9
published
an
article
6 months ago
Article
Blazingly fast whisper transcriptions with Inference Endpoints
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79
updated
a
dataset
7 months ago
published
an
article
7 months ago
Article
The New and Fresh analytics in Inference Endpoints
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21
upvoted
an
article
8 months ago
Article
Welcome Gemma 3: Google's all new multimodal, multilingual, long context open LLM
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467
liked
a
Space
8 months ago
upvoted
an
article
9 months ago
Article
From Chunks to Blocks: Accelerating Uploads and Downloads on the Hub
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77
upvoted
2
articles
10 months ago
Article
Introducing multi-backends (TRT-LLM, vLLM) support for Text Generation Inference
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75
Article
Train 400x faster Static Embedding Models with Sentence Transformers
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216
upvoted
a
collection
10 months ago
liked
a
Space
11 months ago
posted
an
update
12 months ago
Post
1807
A while ago I started experimenting with compiling the Python interpreter to WASM.
To build a secure, fast, and lightweight sandbox for code execution — ideal for running LLM-generated Python code.
- Send code simply as a POST request
- 1-2ms startup times
Hack away:
https://github.com/ErikKaum/runner
To build a secure, fast, and lightweight sandbox for code execution — ideal for running LLM-generated Python code.
- Send code simply as a POST request
- 1-2ms startup times
Hack away:
https://github.com/ErikKaum/runner