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Qwen2.5: A Party of Foundation Models
- GaggiX 2y ago>our latest large-scale dataset, encompassing up to 18 trillion tokens I remember when GPT-3 was trained on 300B tokens.
- imjonse 2y agoand was considered too dangerous to be released publicly.
- GaggiX 2y agoThe larger GPT-2s were also considered too dangerous to release publicly at first.
- Workaccount2 2y agoI remember be very understanding of it too after seeing the incredible (but absolutely terrible in retrospect) outputs.
- GaggiX 2y agoI wasn't really compelled at the time, nothing has changed.
- baq 2y agothey are dangerous... for folks who need to scrape the web for low background tokens to train their transformers.
- abc-1 2y agoNobody ever really believed this, the truth is rarely in vogue.
- Prbeek 2y agoI remember when Ps2 chips were considered too advanced the US government banned shipment of PlayStations to China lest the PLA gets hold of them
- ekojs 2y agoActually really impressive. They went up from 7T tokens to 18T tokens. Curious to see how they perform after finetuning.
- Flux159 2y agoIt would be nice to have comparisons to Claude 3.5 for the coder model, only comparing to open source models isn’t super helpful because I would want to compare to the model I’m currently using for development work.
- imjonse 2y agoAider will probably have some numbers at https://aider.chat/docs/leaderboards/ https://aider.chat/docs/leaderboards/
- Deathmax 2y agoThey've posted their own run of the Aider benchmark [1] if you want to compare, it achieved 57.1%. [1]: https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2.5/Qwen2.5-Coder/qwen2.5-coder-instruct.jpg https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2.5/Qwen...
- reissbaker 2y agoOof. I'm really not sure why companies keep releasing these mini coding models; 57.1% is worse than gpt-3.5-turbo, and running it locally will be slower than OpenAI's API. I guess you could use it if you took your laptop into the woods, but with such poor coding ability, would you even want to? The Qwen2.5-72B model seems to do pretty well on coding benchmarks, though — although no word about Aider yet.
- diggan 2y agoHere is a comparison of the prompt "I want to create a basic Flight simulator in Bevy and Rust. Help me figure out the core properties I need for take off, in air flight and landing" between Claude Sonnet 3.5 and Qwen2.5-14B-Instruct-Q4_K_M.gguf: https://gist.github.com/victorb/7749e76f7c27674f3ae36d791e20140a https://gist.github.com/victorb/7749e76f7c27674f3ae36d791e20... AFAIK, there isn't any (micro)benchmark comparisons out yet.
- yourMadness 2y ago
- freeqaz 2y ago32B is a nice size for 2x 3090s. That comfortably fits on the GPU with minimal quantization and still leaves extra memory for the long context length. 70B is just a littttle rough trying to run without offloading some layers to the CPU.
- a_wild_dandan 2y ago70B+ models typically run great with my MacBook's 96GB of (V)RAM. I want a Mac Studio to run e.g. llama-405B, but I can't justify the marginal model quality ROI for like $7k or whatever. (But I waaant iiit!)
- tarruda 2y agoYou can get refurbished Mac Studio M1 Ultra with 128GB VRAM for ~ $3k on ebay. M1 ultra has 800GB/s memory bandwidth, same as the M2 ultra. Not sure if 128GB VRAM is enough for running 405b (maybe at 3-bit quant?), but it seems to offer great value for running 70B models at 8-bit.
- a_wild_dandan 2y agoYeah, I would want the 192GB Mac for attempting such hefty models. But I have such basic bitch needs that 405B is overkill haha.
- tarruda 2y ago405b even at low quants would have very low tokens generation speed, so even if you got the 192GB it would probably not be a good experience. I think 405b is the kind of model that only makes sense to run in clusters of A100/H100. IMO it is not worth it, 70b models at q8 are already pretty darn good, and 128gb is more than enough for those.
- a_wild_dandan 2y agoExactly! Have you tried the Phi models? To me, they indicate that we can get much more efficient models. In a few years, 70b on gold standard synthetic data + RL might run circles around SotA. It's such an exciting time to be alive.
- cateye 2y ago> we are inspired by the recent advancements in reinforcement learning (e.g., o1) It is interesting to see what the future will bring when models incorporate chain of thought approaches and whether o1 will get outperformed by open source models.
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- jcoc611 2y agoProbably an ignorant question, but could someone explain why the Context Length is much larger than the Generation Length?
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- dacox 2y agoWhen doing inference for an LLM, there are two stages. The first phase is referred to as "prefill", where the input is processed to create the KV Cache. After that phase, the "decode" phase is called auto-regressively. Each decode yields one new token. This post on [Inference Memory Requirements](https://huggingface.co/blog/llama31#inference-memory-requirements https://huggingface.co/blog/llama31#inference-memory-require...) is quite good. These two phases have pretty different performance characteristics - prefill can really maximize GPU memory. For long contexts, its can be nigh impossible to do it all in a single pass - frameworks like vLLM use a technique called "chunked prefill". The decode phase is compute intensive, but tends not to maximize GPU memory. If you are serving these models, you really want to be able to have larger batch sizes during inference, which can only really come with scale - for a smaller app, you won't want to make the user wait that long. So, long contexts only have to be processed _once_ per inference, which is basically a scheduling problem. But the number of decode passes scales linearly with the output length. If it was unlimited, you could get some requests just _always_ present in an inference batch, reducing throughput for everyone.
- irthomasthomas 2y agoI'm impressed by the scope of this drop. The raw intelligence of open models seems to be falling behind closed. But I think that's because frontier models from openai and anthropic are not just raw models, but probably include stuff like COT, 'best of N', or control vectors.
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