Traceback (most recent call last): File “C:SarahOneMetisobwobwwtext-generation-webuiserver.py”, line 68, in load_model_wrapper shared.model, shared.tokenizer = load_model(shared.model_name, loader) File “C:SarahOneMetisobwobwwtext-generation-webuimodulesmodels.py”, line 78, in load_model output = load_func_maploader File “C:SarahOneMetisobwobwwtext-generation-webuimodulesmodels.py”, line 139, in huggingface_loader config = AutoConfig.from_pretrained(path_to_model, trust_remote_code=shared.args.trust_remote_code) File “C:UsersSarah.condaenvsamberrlibsite-packagestransformersmodelsautoconfiguration_auto.py”, line 928, in from_pretrained config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs) File “C:UsersSarah.condaenvsamberrlibsite-packagestransformersconfiguration_utils.py”, line 574, in get_config_dict config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs) File “C:UsersSarah.condaenvsamberrlibsite-packagestransformersconfiguration_utils.py”, line 629, in _get_config_dict resolved_config_file = cached_file( File “C:UsersSarah.condaenvsamberrlibsite-packagestransformersutilshub.py”, line 388, in cached_file raise EnvironmentError( OSError: E:usersSarah.cachehuggingfacehublmsys–fastchat-t5-3b-v1.0 does not appear to have a file named config.json. Checkout ‘https://huggingface.co/E:usersSarah.cachehuggingfacehublmsys–fastchat-t5-3b-v1.0/None’ for available files.
The error traceback indicates that there was an issue while attempting to load a model using the Hugging Face Transformers library. Let’s break down the traceback to understand the problem:
1. The error originates in the file “server.py” on line 68, where the function “load_model_wrapper” is called.
2. In the “load_model_wrapper” function, the code attempts to load the model using the “load_model” function from “models.py”.
3. In “models.py” on line 78, the “load_model” function tries to invoke “load_func_maploader”.
4. The error occurs in “models.py” on line 139, where the “huggingface_loader” is called, and it tries to load the configuration of the model using the “AutoConfig.from_pretrained” method.
5. The “from_pretrained” method in “configuration_auto.py” (from the Hugging Face Transformers library) on line 928 tries to retrieve the configuration dictionary for the specified pretrained model.
6. In the process, the error arises from “configuration_utils.py” on line 629 in the “_get_config_dict” function. It attempts to access a cached file in the Hugging Face cache directory for the specified model.
7. Finally, the error message states that the file “config.json” is missing in the cache directory for the model named “lmsys-fastchat-t5-3b-v1.0”.
The root cause of the error is that the specified model’s configuration file is missing in the Hugging Face cache directory. This could be due to various reasons, such as an incomplete or unsuccessful download of the model, incorrect model name or path, or issues with the cache itself.
To resolve the problem, you can try the following steps:
1. Check the model name or path: Ensure that the model name or path is correct and points to the desired model. It’s possible that there might be a typo or mistake in the model name.
2. Clear the Hugging Face cache: Try clearing the Hugging Face cache to force a fresh download of the model. You can do this by deleting the cache directory or using the appropriate Hugging Face utility to clear the cache.
3. Download the model again: If the model is not correctly downloaded or cached, try downloading it again using the Hugging Face library’s download functions.
4. Verify the internet connection: If the model is supposed to be downloaded from the Hugging Face model hub, ensure that your internet connection is stable and allows access to the hub.
5. Check permissions: Make sure that the script or process executing the code has the necessary permissions to access and write to the cache directory.
After trying these steps, you should be able to resolve the issue and successfully load the model.
2023-07-16 16:07:15 WARNING:The model weights are not tied. Please use the `tie_weights` method before using the `infer_auto_device` function.
2023-07-16 16:07:15 WARNING:The safetensors archive passed at modelsWizardLM-7B-uncensored-GPTQWizardLM-7B-uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors does not contain metadata. Make sure to save your model with the `save_pretrained` method. Defaulting to ‘pt’ metadata.
2023-07-16 16:07:21 WARNING:skip module injection for FusedLlamaMLPForQuantizedModel not support integrate without triton yet.
These warnings indicate potential issues or limitations when using a specific model and its weights. Let’s break down each warning:
1. **WARNING: The model weights are not tied. Please use the `tie_weights` method before using the `infer_auto_device` function.**
This warning suggests that the model’s weights are not tied properly. In the context of neural networks, “tying weights” means using the same set of parameters for different parts of the model. Tying weights can help reduce the number of parameters and improve model performance.
The `infer_auto_device` function is likely used for inference or prediction with the model. To address this warning, you should use the `tie_weights` method before invoking the `infer_auto_device` function. The `tie_weights` method ensures that the weights are properly tied as required by the model architecture.
2. **WARNING: The safetensors archive passed at modelsWizardLM-7B-uncensored-GPTQWizardLM-7B-uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors does not contain metadata. Make sure to save your model with the `save_pretrained` method. Defaulting to ‘pt’ metadata.**
This warning suggests that the provided `safetensors` archive (in the specified path) does not contain metadata. In the context of PyTorch, metadata typically contains information about the model’s architecture, tokenizer, and other important details.
It appears that the model may not have been saved with the `save_pretrained` method from the Hugging Face Transformers library. To resolve this warning, ensure that the model is saved using the `save_pretrained` method, which ensures all necessary metadata is included in the archive.
3. **WARNING: skip module injection for FusedLlamaMLPForQuantizedModel not support integrate without triton yet.**
This warning seems to be related to using a module called `FusedLlamaMLPForQuantizedModel`. It mentions that integrating this module without Triton is not supported yet.
Triton is likely a specific software or system used for optimization or deployment of models, particularly in the context of inference. If you are not using Triton or are not familiar with it, you might want to investigate its relevance in your current setup.
In summary, to address these warnings:
– Use the `tie_weights` method before using the `infer_auto_device` function to ensure the model weights are tied properly.
– Make sure to save your model with the `save_pretrained` method to include all necessary metadata in the `safetensors` archive.
– If applicable, look into Triton integration if you are using the `FusedLlamaMLPForQuantizedModel` module.
Please note that the exact steps to address these warnings might depend on your specific code and use case. Always refer to the documentation of the libraries and modules you are using for more detailed guidance.
is there a code to tie_weights?
The `tie_weights` method is specific to certain language model architectures, especially in the context of Hugging Face Transformers. It is used to tie the weights of the input embeddings and the output embeddings (usually the final layer) in a language model. Tying weights can help in reducing the number of parameters in the model, leading to more efficient memory usage and potentially better generalization.
Here’s an example of how you can tie the weights of a language model using the Hugging Face Transformers library:
“`python
from transformers import AutoModel, AutoTokenizer
# Replace ‘model_name’ with the name of the model you want to use
model_name = ‘model_name’
# Load the model and tokenizer
model = AutoModel.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Assuming you have a specific layer to tie (e.g., the final layer), you can tie the weights like this:
model.tie_weights()
# Now, both the input and output embeddings are tied, and the model is ready for use.
“`
In this code, `AutoModel.from_pretrained` loads the pre-trained language model, and `AutoTokenizer.from_pretrained` loads the tokenizer corresponding to that model. Then, we call the `tie_weights` method on the `model` instance, which will automatically tie the weights of the appropriate layers.
Please note that not all language models support weight tying, and it might depend on the specific architecture you are using. It’s always a good idea to check the documentation of the specific model you are working with to see if weight tying is supported and recommended for that model.