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That's because the model architecture hasn't been added to huggingface/transformers yet, because it literally was just published today. >>> from transforme

by rfoo 3y ago

That's because the model architecture hasn't been added to huggingface/transformers yet, because it literally was just published today. >>> from transformers import AutoTokenizer, AutoModel >>> model = AutoModel.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True, device='cuda') Here, the "trust_remote_code=True" means "download the model code from huggingface repo 'internlm/internlm-chat-7b'", along with the weight, and run it. If it's False, the library would use builtin model architectures hardcoded in huggingface/transformers and only download the weight. The scary flag is here because, of course, newcomers may not realize that model == code and if you load arbitrary model you are likely executing arbitrary code. Wonder why, for example, you don't remember seeing LLaMA had this on release day? Because they don't use huggingface transformers library and don't use huggingface to distribute their model. You just clone and run their code from GitHub, and... how is this not "trust_remote_code"?

tyfon 3y ago
I've only used llama via llama.cpp. In general I think the python ML stuff is a mess. But I still won't execute code that recommend me to trust arbitrary remote code as the remote code can change at any time, it would be better to wait with the release until it was published to the transformers library or just include it in a clonable repo without the trust_remote_code flag. It is much better to just be able to clone the code and have it locally so you can verify it once and not trust that it won't download any new code suddenly that you haven't been able to look at. trust_remote_code means you have no control really, cloning a repo means you control when new code is added yourself.
- rfoo 3y ago
  Yeah, I agree promoting this usage is as bad as promoting `curl | sh` in README.md. Similar to how you can inspect the content of a `curl | sh` script and then run it, the model is also in a clonable repo, you may just: git clone https://huggingface.co/internlm/internlm-7b-chat and: >>> from transformers import AutoTokenizer, AutoModel >>> model = AutoModel.from_pretrained("./internlm-chat-7b", device='cuda')
  - tyfon 3y ago
    This way is much more palpable for me, thank you for showing :)
m3affan 3y ago
Interesting attack vector. Malicious model codes.
oars 3y ago
Thank you for the explanation.
tomsmeding 3y ago
> 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 one could easily imagine a more appropriate domain-specific model language that your code is compiled to, that can by design not perform any IO. Imagine that a model you distribute is not random Python code that may or may not run a model, but instead the model itself, i.e. the graph encoded in that domain-specific language. Then downloading a random model from some random untrusted place is no different from downloading some random data from some untrusted place: you're going to execute the model, which may DOS you, but nothing much else will happen. Unfortunately the ML world is too stuck in the imperative mindset for this (IMO more sensible) way of doing things. :) [1]: https://www.tensorflow.org/api_docs/python/tf/io/write_file https://www.tensorflow.org/api_docs/python/tf/io/write_file
- 411111111111111 3y ago
  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
  - DougBTX 3y ago
    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++.

by rfoo 3y ago

tyfon 3y ago

I've only used llama via llama.cpp. In general I think the python ML stuff is a mess. But I still won't execute code that recommend me to trust arbitrary remote code as the remote code can change at any time, it would be better to wait with the release until it was published to the transformers library or just include it in a clonable repo without the trust_remote_code flag. It is much better to just be able to clone the code and have it locally so you can verify it once and not trust that it won't download any new code suddenly that you haven't been able to look at. trust_remote_code means you have no control really, cloning a repo means you control when new code is added yourself.

rfoo 3y ago
Yeah, I agree promoting this usage is as bad as promoting `curl | sh` in README.md. Similar to how you can inspect the content of a `curl | sh` script and then run it, the model is also in a clonable repo, you may just: git clone https://huggingface.co/internlm/internlm-7b-chat and: >>> from transformers import AutoTokenizer, AutoModel >>> model = AutoModel.from_pretrained("./internlm-chat-7b", device='cuda')
- tyfon 3y ago
  This way is much more palpable for me, thank you for showing :)

m3affan 3y ago

Interesting attack vector. Malicious model codes.

oars 3y ago

Thank you for the explanation.

tomsmeding 3y ago

> 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 one could easily imagine a more appropriate domain-specific model language that your code is compiled to, that can by design not perform any IO. Imagine that a model you distribute is not random Python code that may or may not run a model, but instead the model itself, i.e. the graph encoded in that domain-specific language. Then downloading a random model from some random untrusted place is no different from downloading some random data from some untrusted place: you're going to execute the model, which may DOS you, but nothing much else will happen. Unfortunately the ML world is too stuck in the imperative mindset for this (IMO more sensible) way of doing things. :) [1]: https://www.tensorflow.org/api_docs/python/tf/io/write_file https://www.tensorflow.org/api_docs/python/tf/io/write_file

411111111111111 3y ago
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
- DougBTX 3y ago
  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++.