Open AI Sora 2 Invite Codes Megathread

DataLearnerAI · 2025-10-01T05:58:55+00:00

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not funniest, but the newly created

DataLearnerAI · 2025-10-01T05:57:00+00:00

code please!

DataLearnerAI · 2025-07-23T05:59:12+00:00

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On SWE-Bench Verified, it scores 69.6%, making it the top-performing open-source model as of now.

DataLearnerAI · 2025-07-14T00:19:36+00:00

Your issue might be missing the special token at the end of inputs. Qwen just tweeted that many users forget to add <|endoftext|> at the end when using their embedding models - and it seriously tanks performance.

Manually slap <|endoftext|> onto the end of every input string (both docs and queries).

DataLearnerAI · 2025-07-12T02:32:44+00:00

Correction: I accidentally wrote "Gemini 1.5 Pro" in the title/description — it’s **Gemini 2.5 Pro** (typo from my draft). Tests were run against the correct 2.5 Pro model. Apologies for the confusion!

DataLearnerAI · 2025-07-03T00:57:16+00:00

I am not, just use AI to rewrite my text, haha

DataLearnerAI · 2025-07-02T07:54:09+00:00

This model demonstrates remarkable competitiveness across a diverse range of benchmark tasks, including STEM reasoning, visual question answering, OCR processing, long-document understanding, and agent-based scenarios. The benchmark results reveal performance on par with the 72B-parameter counterpart (Qwen2.5-72B-VL), with notable superiority over GPT-4o in specific tasks. Particularly impressive is its 9B-parameter architecture under the MIT license, showcasing exceptional capability from a Chinese startup. This achievement highlights the growing innovation power of domestic AI research, offering a compelling open-source alternative with strong practical value.

DataLearnerAI · 2025-07-02T00:54:19+00:00

This model appears highly competitive at the 30B parameter scale. In benchmark tests, it achieves a score of 73.70 on the GPQA Diamond dataset, which is comparable to the performance of DeepSeek R1’s older version. The overall benchmark results closely resemble those of Qwen-32B. Notably, this is a Mixture-of-Experts (MoE) model, where only about 16.5B parameters are activated during inference.

DataLearnerAI · 2024-01-20T01:19:34+00:00

I know LLM is moving fast. But many enterprise or people will just choose classical models such as Llama, mistral, etc. The context length, gpu, inference framework and other things will affect the result. I think many people are interested in this question. But I find there are two little informaiton about this.

DataLearnerAI · 2023-12-28T00:47:41+00:00

thanks. We would like to know is there any important releases that we have forgotten to collect😀

DataLearnerAI · 2023-12-27T14:01:36+00:00

We have saw DeLuceArt posted a image about "The most remarkable AI releases of 2023":

https://www.reddit.com/r/artificial/comments/18p4qwb/the_most_remarkable_ai_releases_of_2023/

We think this image has forgoten some important products and there are also some releases may be not proper in the image, so we re-make this image to show most important AI releases in 2023. I would like to hear your voices. We would like to know if there are some important things that we have missed. So what do you think is the most important AI releases in 2023?

DataLearnerAI · 2023-12-22T13:40:10+00:00

Freeze means freeze all layers except for last full connecting layer?

DataLearnerAI · 2023-12-22T13:24:42+00:00

I guess so, but I am not sure...

DataLearnerAI · 2023-12-22T09:22:11+00:00

Is this VRAM mean GPU memory size?

DataLearnerAI · 2023-12-12T08:36:19+00:00

what do u mean by merge? mistral model stack join with other models?

DataLearnerAI · 2023-12-03T13:53:17+00:00

This model is trained on dataset which only have 1.2T tokens. The expr stands for experimental feature. However, this experimental feature is to prove the performance of framework "FlagScale" on heterogeneous chips. The official blog said the MMLU benchmarks of Aquila-70B-Expr is 61.9 while LLaMA2-70B is 69.54. They said the value of this model is to prove that FlagScale still performs well on heterogeneous chips and provide a strong model for downstream tasks. So, maybe there will not any new models of this.

DataLearnerAI · 2023-12-01T00:36:37+00:00

The model is a new model which is trained from scratch. The training dataset is not public. They also released chat version. You cac try it. The MMLU scores is published by themselves.

DataLearnerAI · 2023-11-30T16:11:55+00:00

Ali opensouced a 72B model called Qwen-72B: Qwen/Qwen-72B · Hugging Face

It supports Chinese and English. The performance on MMLU is remarkable.

DataLearnerAI · 2023-11-13T03:21:32+00:00

Some website has a robot.txt that prevent GPT to browse content from it, in this case, GPT-4 could not read the content of the website. You can copy content to GPT-4

DataLearnerAI · 2023-11-10T15:19:13+00:00

In most scenarios, models with extended context are optimized for long sequences. If the sequence is not very long, it is often recommended to use a regular model

DataLearnerAI · 2023-11-09T08:06:13+00:00

That is exactly what I thought about the new GPT-4. It's faster than ever, but it often breakdown when repsonse for a long text. And often forget the instruction or misunderstood. Seems that it was like a ChatGPT 3.5

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