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CodeTF
Python2023–2023
salesforce / CodeTF · Archived

CodeTF

CodeTF: One-stop Transformer Library for State-of-the-art Code LLM

3Distinct people
3Mentions
0Explicit recommenders
1.5KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

CodeTF: One-stop Transformer Library for State-of-the-art Code LLM

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 12:54:37 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by hour3 dated mentions

Observed mentions: 2023-06-07 11:20:22 UTC to 2023-06-07 23:01:16 UTC.

012023-06-07 11:00:00 UTC to 2023-06-07 12:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsJun 7, 11:002023-06-07 12:00:00 UTC to 2023-06-07 13:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-06-07 13:00:00 UTC to 2023-06-07 14:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-06-07 14:00:00 UTC to 2023-06-07 15:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-06-07 15:00:00 UTC to 2023-06-07 16:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-06-07 16:00:00 UTC to 2023-06-07 17:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-06-07 17:00:00 UTC to 2023-06-07 18:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsJun 7, 17:002023-06-07 18:00:00 UTC to 2023-06-07 19:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-06-07 19:00:00 UTC to 2023-06-07 20:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-06-07 20:00:00 UTC to 2023-06-07 21:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-06-07 21:00:00 UTC to 2023-06-07 22:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-06-07 22:00:00 UTC to 2023-06-07 23:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-06-07 23:00:00 UTC to 2023-06-08 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJun 7, 23:00

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Jun 7, 11:002023-06-07 11:00:00 UTC2023-06-07 12:00:00 UTC110
Jun 7, 12:002023-06-07 12:00:00 UTC2023-06-07 13:00:00 UTC000
Jun 7, 13:002023-06-07 13:00:00 UTC2023-06-07 14:00:00 UTC000
Jun 7, 14:002023-06-07 14:00:00 UTC2023-06-07 15:00:00 UTC000
Jun 7, 15:002023-06-07 15:00:00 UTC2023-06-07 16:00:00 UTC000
Jun 7, 16:002023-06-07 16:00:00 UTC2023-06-07 17:00:00 UTC000
Jun 7, 17:002023-06-07 17:00:00 UTC2023-06-07 18:00:00 UTC000
Jun 7, 18:002023-06-07 18:00:00 UTC2023-06-07 19:00:00 UTC000
Jun 7, 19:002023-06-07 19:00:00 UTC2023-06-07 20:00:00 UTC000
Jun 7, 20:002023-06-07 20:00:00 UTC2023-06-07 21:00:00 UTC000
Jun 7, 21:002023-06-07 21:00:00 UTC2023-06-07 22:00:00 UTC000
Jun 7, 22:002023-06-07 22:00:00 UTC2023-06-07 23:00:00 UTC101
Jun 7, 23:002023-06-07 23:00:00 UTC2023-06-08 00:00:00 UTC101

What Hacker News says

3 sources
salesforce really seems to invest a lot of resources into coding focused applications for LLMs, which is of course great, especially as they seem very transparent, sharing both papers and usable implementations[0]. However, I feel that I am really starting to lose track over the differences in their releases (T5 vs T5+ vs Gen vs this), especially as they come so fast. Perhaps this reflects poorly on me, but I find it hard to really stay up to date with the consistent stream of preprints and releases (not just from salesforce) as it tends to take me a while before fully internalizing what makes the newest developments so special, so I was very happy to find that they added an overview that compares different supported models (including their releases) to the newest repo[1]. Of course, size does not correlate with performance, but it still
Read the rest
helped me to get a better grip on what they mean by "one-stop Python transformer-based library for code large language models (Code LLMs) and code intelligence" and how that relates to existing models. CodeTF very grossly oversimplified, intends to make working with models, in a multitude of ways, easier. [0] https://github.com/orgs/salesforce/repositories?q=llm [1] https://github.com/salesforce/CodeTF
In CodeTF: One-Stop Transformer Library for State-of-the-Art Code LLM
Link to the github repo: https://github.com/salesforce/CodeTF It would be helpful to see some Colab notebook examples of how I could use this or incorporate my own codebase with these open source coding models. The examples show some smaller interesting prediction task & translation between csharp and java, but probably easier to try it out in Colab than having to install locally. I would also want to be able to compare Github Copilot's autocomplete with what CodeT5 would return as a prediction.
In CodeTF: One-Stop Transformer Library for State-of-the-Art Code LLM

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