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After publishing my first translation article and discussing it on Reddit, two suggestions appeared repeatedly in the replies: use a model trained specifically for translation; constrain the response through structured decoding and a JSON Schema.
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I was qualifying exact checkpoint resumption for a Qwen3.5-0.8B Base training path in Open-Instruct.
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The project stopped being a throughput project when I opened one of the fast outputs and found an answer.
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I came to verl for a research project: RL on function calling.
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Introduction uv’s documentation about build isolation is already great and has everything you need and I recommend and suggest to read before anything else.
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Hi everyone!
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My first two articles (part 1 and part 2) on Python project management and packaging gathered a lot of interest and I thought they were comprehensive enough for me not to come back and update them for a moment.
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In the first part we delved into the nitty gritty details of initializing and managing the dependencies of a Python package or project.
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The goal of this guide is to provide a comprehensive guide to Python project management and packaging.
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The goal of this article is to provide a clear step-by-step guide starting from the fundamentals for deploying a Streamlit app on an AWS EC2 Instance and use your own domain name as well.
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This post will only focus on some position encoding, namely, sinusoidal, T5 bias, ALiBi and Rotary.
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Paper: Generating Long Sequences with Sparse Transformers Before diving into this paper, I’ll have to say that this article might change in the future to include from scratch (but still relying on sparse backend operations) implementations of the theoreti…
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(Part I) Modeling language can be done using a wide variety of techniques (e.g.
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All the code in this article can be found on ReinforcedKnowledge/deep-learning-from-scratch/transformer Background The Transformer comes as an answer to the sequential computation constraint that recurrent neural networks (RNNs), long short-term memory ne…