fix(docs): correct typos (#662)
* docs: fix typos * Update CHANGELOG.md * Update README.md * Update README.md * Update index.mdrelease-notes-05
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@ -78,6 +78,6 @@ Keybindings~
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`<Tab>` if no completion is shown.
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<C-\> Trigger completion if not shown. Dismiss the current
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compleiton if shown.
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completion if shown.
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vim:tw=78:ts=8:noet:ft=help:norl:
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@ -80,7 +80,7 @@ This is where the concept of stream laziness comes into play. We should perform
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## How to handle canellation?
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## How to handle cancellation?
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The core idea is straightforward: on the server side, we need to listen to the `close` event and check if the connection is still valid before pulling data from the LLM stream.
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@ -15,7 +15,7 @@ Today, Tabby stands out as the most popular and user-friendly solution to enable
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## Release v0.3.0 - Retrieval Augmented Code Completion 🎁
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Tabby also comes to a [v0.3.0 release](https://github.com/TabbyML/tabby/releases/tag/v0.3.0), with the support of retrieval-augmented code completion enabled by default. Enhanced by repo-level retrieval, Tabby gets smarter at your codebase and will quickly reference to a related funcion / code example from another file in your repository.
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Tabby also comes to a [v0.3.0 release](https://github.com/TabbyML/tabby/releases/tag/v0.3.0), with the support of retrieval-augmented code completion enabled by default. Enhanced by repo-level retrieval, Tabby gets smarter at your codebase and will quickly reference to a related function / code example from another file in your repository.
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A blog series detailing the technical designs of retrieval-augmented code completion will be published soon. Stay tuned!🔔
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@ -7,7 +7,7 @@ image: ./twitter-decoding.png
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---
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# Decode the Decoding in Tabby
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In the context of the Transformer model, which is widely used across LLMs, ***decoding*** refers to the process of generating an output sequence from an encoded input. Tabby recenty [implemented ***incremental decoding***](https://github.com/TabbyML/tabby/pull/491) as part of the greedy search. This blog will explain our thoughts behind this 🛠️💡.
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In the context of the Transformer model, which is widely used across LLMs, ***decoding*** refers to the process of generating an output sequence from an encoded input. Tabby recently [implemented ***incremental decoding***](https://github.com/TabbyML/tabby/pull/491) as part of the greedy search. This blog will explain our thoughts behind this 🛠️💡.
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## Common Decoding Methods
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@ -21,7 +21,7 @@ numbers = [1, 2, 3, 4, 5]
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evens = [x for x in numbers
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```
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To simplify the scenario, we assume that the language model maintains a probaility distribution as shown below,
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To simplify the scenario, we assume that the language model maintains a probability distribution as shown below,
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@ -67,6 +67,6 @@ In the case above, the final decoded string would be `" he llo"` with an awkward
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Incremental decoding: ......, 207, 211 -> "......[ hello]" ✅
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```
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For interested folks, you can refer to Tabby's exact implementation in `IncrementalDecoding` funcion in [`creates/tabby-inference/src/decoding.rs`](https://github.com/TabbyML/tabby/pull/491).
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For interested folks, you can refer to Tabby's exact implementation in `IncrementalDecoding` function in [`creates/tabby-inference/src/decoding.rs`](https://github.com/TabbyML/tabby/pull/491).
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Have you found our new decoding methods effective? Share your thoughts with us in our [Slack](https://join.slack.com/t/tabbyml/shared_invite/zt-22thejc0z-7ePKeWNCHPX31pEtnT4oYQ) channel 🌍😊!
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@ -11,7 +11,7 @@ This tutorial is now also available on [Hugging Face](https://huggingface.co/doc
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## Your first Tabby Space
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In this section, you will learn how to deploy a Tabby Space and use it for yourself or your orgnization.
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In this section, you will learn how to deploy a Tabby Space and use it for yourself or your organization.
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### Deploy Tabby on Spaces
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