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InternLM – new open source 7B LLM

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Re: InternLM – new open source 7B LLM

#51

Post by ohgodplsno »

version_five wrote: post #36
ohgodplsno wrote: post #17

Earlier quoted context omitted.

Less excited that you can't freely take work from academics to resell it in a shitty SaaS company ?

So you don't use open source software? You should try it, there's a great ecosystem of free software, including lots written by academics who are happy to have their work add value to industry

I do. And I also don't use open-source software with a commercial license for my job, because I respect the wishes of the author. It doesn't make the projects, tools and libraries any less good. OP is just looking to quickly cash in to something he didn't put an ounce of effort in.

AGPL & Dual-licensing are the way forward, because of leeches.

Re: InternLM – new open source 7B LLM

#52

Post by logicchains »

gnrlst wrote: post #3

Is this also censored/nerfed? I'd love to play with a "raw" unnerfed model to fully grasp what an LLM can do (and see how biased it is). Does anyone have any recommendations for unnerfed models to try out?

LLaMA 65B is the best uncensored model we've got, and the Airoboros fine-tuning if you want it to follow instructions.

Re: InternLM – new open source 7B LLM

#54

Post by ohgodplsno »

exizt88 wrote: post #30
ohgodplsno wrote: post #17

Earlier quoted context omitted.

Less excited that you can't freely take work from academics to resell it in a shitty SaaS company ?

You do understand that academics are usually funded by taxpayers? Obviously, not by me, as I don't pay taxes in China, but it's not like academics are doing this work for free. Society pays them for their work so that it can benefit from its results.

You do understand that private companies, as a whole, are a drain on the academic system, pushing to lower the very taxes that fund this research ? Society should benefit from these results. The 5000th LLM-haiku-generator-saas-company-incorporated-in-delaware is not society.

Re: InternLM – new open source 7B LLM

#55

Post by _ea1k »

rfoo wrote: post #15
jabbany wrote: post #10

Earlier quoted context omitted.

Seems pretty common though, for defining custom architecture configs whatnot? AFAIK the "remote code" is still openly hosted on huggingface so you can audit it if you like. Seems no more dangerous than things like `pip install some_random_library`?

I like this pip metaphor. If we had required `--trust-remote-code` for every `npm install` we could have avoided left-pad and most of the software supply chain drama in the past years.

I think that would just teach people to type --trust-remote-code fast.

Re: InternLM – new open source 7B LLM

#56

Post by DougBTX »

411111111111111 wrote: post #46
tomsmeding wrote: post #44

Earlier quoted context omitted.

> newcomers may not realize that model == code This makes sense in a way given the API of typical ML libraries. But there is no fundamental reason this needs to be the case. Or, more correctly stated: model == code for sure, but said code need not have any rights to perform side effects. For some reason e.g. TensorFlow has stuff like tf.io.write_file [1] (is that actually an operation you can put in a model???), but…

At that point you'd need a machine learning DSL and runtime. Currently, it's all python libraries, so you can do everything python can... Which is everything, essentially. It's highly unlikely that the market for running these models like an appliance securely in an untrusted context will ever manifest. It's just too much of a niche, as it would also reduce their extensibility/usability significantly

Something like this may grow out of the GGML project, which is gaining traction. They already have a weights format which can be loaded with mmap, though AFAIK the model architecture still needs to be defined in C++.

Re: InternLM – new open source 7B LLM

#57

Post by huggingmouth »

exizt88 wrote: post #23
hmottestad wrote: post #16

Earlier quoted context omitted.

Is that even valid? This seems to be the only place where they’ve made this exemption, it’s not written in the license. Even the weights on hugging face are licensed under apache 2.0. Doesn’t apache 2.0 allow for fairly unrestricted commercial use? Isn’t that the whole point of using that license?

The model code is Apache 2.0, the weights are proprietary.

That's not their huggingface repo says: https://huggingface.co/internlm/internlm-7b

The current release on huggingface is available under plain apache 2.0

Re: InternLM – new open source 7B LLM

#58

Post by potency »

airgapstopgap wrote: post #11

Note that this is apparently a 7B version of a 104B model trained with the intention of competing with OpenAI offerings on the Chinese market [1]. There is a number of those projects: Baichuan, ChatGLM2, InternLM and some more iirc, and they all have small-scale opensource versions. For what it's worth, I've tried out ChatGLM2-6B and Baichuan converted to LLaMA (the architecture is literally identical in that case).…

I don't know anything about what you're talking about. Where do I start to learn some of the AI terminology, models, benefits and drawbacks of each, etc?

Re: InternLM – new open source 7B LLM

#59

Post by brucethemoose2 »

airgapstopgap wrote: post #11

Note that this is apparently a 7B version of a 104B model trained with the intention of competing with OpenAI offerings on the Chinese market [1]. There is a number of those projects: Baichuan, ChatGLM2, InternLM and some more iirc, and they all have small-scale opensource versions. For what it's worth, I've tried out ChatGLM2-6B and Baichuan converted to LLaMA (the architecture is literally identical in that case).…

> Surprisingly, they do not appear censored in any particularly "Chinese" political direction, but they share sensibilities of ChatGPT and Claude.

Perhaps they used GPT4 responses for the instruct finetuning, as many LLaMA finetunes do?

The paper doesn't say where they got the data from, other than "The pre-trained language model is further fine-tuned, following the mainstream procedure as in InstructGPT."

(Also, I don't like how they use raw LLaMA 65b as a benchmark rather than an instruct tuned derivative)

Re: InternLM – new open source 7B LLM

#60

Post by Tostino »

exizt88 wrote: post #23
hmottestad wrote: post #16

Earlier quoted context omitted.

Is that even valid? This seems to be the only place where they’ve made this exemption, it’s not written in the license. Even the weights on hugging face are licensed under apache 2.0. Doesn’t apache 2.0 allow for fairly unrestricted commercial use? Isn’t that the whole point of using that license?

The model code is Apache 2.0, the weights are proprietary.

If weights are even protected by copyright at all...which would be a departure from current law.
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