Bilibili's New Translation Model Runs on My 8GB Laptop - I Tested the Memes
Read the Portuguese translation →
Author: Prompt Engineer 48 · Watch on YouTube
Video authored by the channel shown, not by IANIA; all rights belong to the creator. Transcript and summary generated automatically; the original-language content is preserved.
Original language: English
Original video: Bilibili’s New Translation Model Runs on My 8GB Laptop - I Tested the Memes · Channel: Prompt Engineer 48 · 03:07 · Watch on YouTube
Topics: Model inference and optimization, AI models, Local and open-source models, Multimodal models
Summary
Section titled “Summary”The video tests BiliBili’s Index Translate model locally on an 8 GB laptop GPU, covering its features, setup, translation quality, and speed. The model handles several languages, internet memes, and structured instructions, although it misses some sarcasm. The reviewer reports about 44 tokens per second and around 6 GB of video memory use with a 4,000-token context. The video also notes that Bilibili offers a website demo for people who do not want to set up the model.
Key points
Section titled “Key points”- Index Translate is a BiliBili model family released under the Apache 2.0 license, with a main text model supporting 150 languages.
- The text model comes in 2B, 9B, and 35B preview sizes; related models handle speech translation and dubbing, syllable control, and long documents.
- The reviewer ran the recommended Q4KM GGUF model, about 5.78 GB, on an 8 GB laptop GPU using Ollama.
- Basic tests from English to Chinese, Spanish, and Japanese, as well as Chinese to English, produced clean results, though one Chinese line had an awkward phrasing.
- The model translated several Chinese internet memes and slang effectively, but failed to preserve the sarcasm in one example.
- It translated test sentences in Nepali, Telugu, Hindi, and Swahili into sensible English.
- Instruction-following tests showed it could use glossary terms, preserve HTML tags and a name placeholder, and produce formal French.
- In one test, it produced an English sentence with exactly eight syllables; the reviewer cautions that this is only one test, not a benchmark.
- Reported performance was about 44 tokens per second and about 6 GB of video memory with a 4,000-token context.
Chapters
Section titled “Chapters”| Start | Chapter | Summary |
|---|---|---|
| 00:00 | Introducing Index Translate | The video introduces the model, its language support and variants, and BiliBili’s published evaluation figures. |
| 01:02 | Local setup and basic translations | The reviewer describes downloading the recommended GGUF model and tests several basic translation directions. |
| 01:33 | Memes, low-resource languages, and instructions | The tests cover Chinese memes and slang, several other languages, and how well the model follows formatting and style requests. |
| 02:36 | Syllable test, speed, and demo | The reviewer checks an eight-syllable instruction, reports local performance, and points viewers to BiliBili’s online demo. |
Quotes
Section titled “Quotes”running completely on my 8 GB laptop GPU. — 00:00
the one they recommend is Q4KM at 5.78 GB — 01:02
So, the sarcasm got lost. It’s good, not magic. — 02:05
It gave today’s weather is truly fine. — 02:36
about 44 tokens per second and about 6 GB of video memory — 02:36
Concepts and entities
Section titled “Concepts and entities”Index Translate, BiliBili, Index Echo, Index Homura, Index Nylong, Ollama, Hugging Face, GGUF, Apache License 2.0
Full transcript (original language)
Section titled “Full transcript (original language)”00:00 All right. In this video, we will see Index Translate, BiliBili’s new open translation model, running completely on my 8 GB laptop GPU. And you can see that it handles Chinese internet memes and tricky instructions, not just plain sentences. Let’s go ahead and test this out. So, what is it? Index Translate is a model family from the Index team at BiliBili, released on September 30th under the Apache 2.0 license. The main text model supports 150 languages, and it comes in three sizes, 2 billion, 9
00:30 billion, and a 35 billion preview. Now, on top of that, they trained three more models. Index Echo does speech translation and dubbing. Index Homura controls how many syllables the translation has, so it fits a dubbing window. And Index Nylong translates long documents in one pass. On the numbers they published, the 9B model scores 0.8789 on Floors with Comet 22, and 0.6771 on their instruction following test. These are BiliBili’s own figures, so take them as a good start, and then we will check it ourselves. For running it
01:02 locally, they released official GGUF files on October 3rd. The sizes go from 3.91 GB up to 18.41, and the one they recommend is Q4KM at 5.78 GB, that fits on my 8 GB card. So, what I’m going to do is pull it with Ollama straight from Hugging Face. It’s about 5.8 GB, and on my connection that took around 6 minutes. One thing to know, this model likes to think before it answers. So, I turned thinking off and set the temperature to zero, which is what the model card recommends. The first load
01:33 took about half a minute, and after that it’s quick. Let’s start with the basics, English to Chinese, English to Spanish, English to Japanese, and Chinese back to English. And you can see these all come out clean. One small thing, in the Chinese line, it wrote the memory as 8,000 MB and turned fits in into takes up, so that reads a bit off. Now, the part I really wanted to see, the memes, the slang Joe Biden became absolutely amazing, Totally worth raving about. Zundu Jiadu became really you actually finished the code overnight. You’re my hero. And Pofan came out as completely
02:05 broke me. That’s really good. But it’s not perfect. The sarcastic one, Xuan Q, where someone thanks you for eating their takeout, came out as thanks a lot. I’m really grateful. So, the sarcasm got lost. It’s good, not magic. Now, low resource languages, I wrote the same sentence in Nepali, Telugu, and Hindi, and all three came back as the same sensible English. Swahili worked, too, with a slightly different sentence. And that’s really great for a model that runs on a laptop. Then instruction following, which is their big selling point. I gave it a glossary and it used
02:36 both of my fixed terms. I asked it to keep the HTML tags and a name placeholder, and the tags and curly braces stayed untouched. And I asked for formal French, and it came back polite with please and thank you. I also asked for exactly eight syllables in English. It gave today’s weather is truly fine. And if you count it, that is eight. Though, to be fair, that’s one test, not a benchmark. And the speed, about 44 tokens per second and about 6 GB of video memory with a 4,000 token context on a laptop. That’s more than fast
03:07 enough for everyday translating. If you don’t want to set anything up, Bilibili hosts a demo on the website with quick buttons like polite Japanese JSON output and meme packs such as Gen Z slang and crazy Thursday. So, in this video, we have seen Index Translate running on my own laptop. If you’re interested, go ahead and try it on your own system as and let me know how it goes. I’ll see you in another one.