E:.cachehuggingface
Would you like to continue the script? (y/n): y
Downloading tokenizer.model: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 500k/500k [00:00<00:00, 2.44MB/s]
Downloading (…)/main/tokenizer.json: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.84M/1.84M [00:00<00:00, 2.65MB/s]
Downloading (…)cial_tokens_map.json: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 435/435 [00:00<?, ?B/s]
Downloading (…)lve/main/config.json: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 595/595 [00:00<?, ?B/s]
Traceback (most recent call last):
File "C:SarahOneLucyvicuna30bdl.py", line 29, in
main()
File “C:SarahOneLucyvicuna30bdl.py”, line 24, in main
model = AutoModelForCausalLM.from_pretrained(“TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ”, device=device)
File “c:ProgramDataminiconda3envsgencontainerlibsite-packagestransformersmodelsautoauto_factory.py”, line 484, in from_pretrained
return model_class.from_pretrained(
File “c:ProgramDataminiconda3envsgencontainerlibsite-packagestransformersmodeling_utils.py”, line 2555, in from_pretrained
raise EnvironmentError(
OSError: TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ does not appear to have a file named pytorch_model.bin, tf_model.h5, model.ckpt or flax_model.msgpack.
It seems like there was an error while trying to load the model. The error message suggests that the model file “pytorch_model.bin” or any of the other expected model files are missing for the “TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ” model.
Please make sure that the model files are correctly downloaded and located in the appropriate directory. Double-check the file names and their locations to ensure they match the expected file structure for the model you are trying to load.
If you continue to experience issues, please provide more details about the steps you followed and any additional error messages you received, and I’ll do my best to assist you further.
tHIS SCRIPT BROKE: import os
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
def cache_location():
# Get the value of the HF_HOME environment variable
cache_location = os.environ.get(‘HF_HOME’, os.path.join(os.path.expanduser(‘~’), ‘.cache’, ‘huggingface’))
print(‘The Hugging Face cache is located at:’, cache_location)
def continue_script():
answer = input(‘Would you like to continue the script? (y/n): ‘)
if answer == ‘y’:
return True
else:
return False
def main():
cache_location()
from transformers import AutoTokenizer, AutoModelForCausalLM
if continue_script():
tokenizer = AutoTokenizer.from_pretrained(“TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ”)
model = AutoModelForCausalLM.from_pretrained(“TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ”)
else:
print(‘Exiting script…’)
if __name__ == ‘__main__’:
main()
The issue with your script is that the import statement for the `AutoTokenizer` and `AutoModelForCausalLM` classes is placed inside the `main()` function instead of being at the beginning of the script. This causes a NameError when trying to access these classes outside of the `main()` function.
To fix the script, move the import statement for `AutoTokenizer` and `AutoModelForCausalLM` to the top of the script, before the `main()` function. Here’s the corrected version of your script:
“`python
import os
from transformers import AutoTokenizer, AutoModelForCausalLM
def cache_location():
# Get the value of the HF_HOME environment variable
cache_location = os.environ.get(‘HF_HOME’, os.path.join(os.path.expanduser(‘~’), ‘.cache’, ‘huggingface’))
print(‘The Hugging Face cache is located at:’, cache_location)
def continue_script():
answer = input(‘Would you like to continue the script? (y/n): ‘)
if answer == ‘y’:
return True
else:
return False
def main():
cache_location()
if continue_script():
tokenizer = AutoTokenizer.from_pretrained(“TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ”)
model = AutoModelForCausalLM.from_pretrained(“TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ”)
else:
print(‘Exiting script…’)
if __name__ == ‘__main__’:
main()
“`
Now the `AutoTokenizer` and `AutoModelForCausalLM` classes are imported correctly, and the script should run without any import errors.
Exception has occurred: OSError
TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ does not appear to have a file named pytorch_model.bin, tf_model.h5, model.ckpt or flax_model.msgpack.
File “C:SarahOneLucyvicuna30bdl.py”, line 20, in main
model = AutoModelForCausalLM.from_pretrained(“TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ”)
File “C:SarahOneLucyvicuna30bdl.py”, line 25, in
main()
OSError: TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ does not appear to have a file named pytorch_model.bin, tf_model.h5, model.ckpt or flax_model.msgpack.
The error message indicates that the model files for the “TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ” model are missing. The model files are typically stored in a directory named after the model. In this case, the required file is `pytorch_model.bin`, `tf_model.h5`, `model.ckpt`, or `flax_model.msgpack`, depending on the framework.
To resolve this issue, make sure you have correctly downloaded and saved the model files in the appropriate directory. Ensure that you have the necessary permissions to access the files and that they are located in the expected file structure.
Here are the steps you can follow to download and save the model files:
1. Create a directory named `TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ` in your desired location.
2. Download the model files (`pytorch_model.bin`, `tf_model.h5`, `model.ckpt`, or `flax_model.msgpack`) from the model’s repository or Hugging Face model hub.
3. Place the downloaded files into the `TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ` directory.
Make sure the model files are placed in the correct location, and then run your script again. It should be able to load the model without encountering the “OSError” you faced previously.
a simple prompt loop please: from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained(“CalderaAI/30B-Lazarus-GPTQ4bit”)
model = AutoModelForCausalLM.from_pretrained(“CalderaAI/30B-Lazarus-GPTQ4bit”)
Certainly! Here’s a simple prompt loop that uses the tokenizer and model you provided:
“`python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained(“CalderaAI/30B-Lazarus-GPTQ4bit”)
model = AutoModelForCausalLM.from_pretrained(“CalderaAI/30B-Lazarus-GPTQ4bit”)
while True:
# Get user input
user_input = input(“Enter your prompt (or ‘q’ to quit): “)
# Check if user wants to quit
if user_input.lower() == “q”:
print(“Exiting prompt loop…”)
break
# Tokenize the input
input_ids = tokenizer.encode(user_input, return_tensors=”pt”)
# Generate output from the model
output = model.generate(input_ids=input_ids, max_length=100, num_return_sequences=1)
# Decode and print the generated text
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(“Generated text:”, generated_text)
“`
This code sets up a prompt loop where it continuously prompts the user for input until they enter “q” to quit. It uses the tokenizer to encode the user input and passes the encoded input to the model for text generation. The generated text is then decoded and printed to the console.
Feel free to modify the code to suit your specific requirements or add more functionality as needed.
I HAVE TOKENIZERS INSTALLED! ProgramDataminiconda3envslilithlibsite-packagestransformersutilsimport_utils.py”, line 1100, in requires_backends
raise ImportError(“”.join(failed))
ImportError:
LlamaTokenizerFast requires the 🤗 Tokenizers library but it was not found in your environment. You can install it with:
“`
pip install tokenizers
“`
In a notebook or a colab, you can install it by executing a cell with
“`
!pip install tokenizers
“`
Please note that you may need to restart your runtime after installation.
(lilith) C:SarahOneLucy>
It seems that you have encountered an error indicating that the required “tokenizers” library is not installed in your environment. To resolve this issue, you can follow the instructions provided in the error message and install the “tokenizers” library.
If you are using a Conda environment named “lilith,” you can open your command prompt and run the following command to install the library:
“`
pip install tokenizers
“`
Once the installation is complete, you should be able to run your code without encountering the “ImportError” related to the “tokenizers” library.
If you are working in a notebook or a Colab environment, you can install the library by executing a cell with the following command:
“`
!pip install tokenizers
“`
After installing the library, you might need to restart your runtime or kernel before running your code again.
Make sure to install the “tokenizers” library in the correct environment, and then you should be able to use the tokenizer without any issues.
│ 415 │ try: │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
OSError: Could not locate pytorch_model-00001-of-00007.bin inside CalderaAI/30B-Lazarus-GPTQ4bit.
The error message indicates that the file “pytorch_model-00001-of-00007.bin” could not be located inside the “CalderaAI/30B-Lazarus-GPTQ4bit” model. It seems that the specific file required by the model is missing.
To resolve this issue, make sure you have downloaded the model files correctly and that they are saved in the expected file structure. The “pytorch_model-00001-of-00007.bin” file should be located within the “CalderaAI/30B-Lazarus-GPTQ4bit” directory.
If you have already downloaded the model files but are still encountering this error, double-check the exact names and locations of the files. It’s possible that there was an issue during the download or that the file names have been changed.
If you continue to face difficulties, I recommend reaching out to the model provider or the community that supports the model for further assistance. They will be able to provide you with specific guidance on how to properly set up and use the “CalderaAI/30B-Lazarus-GPTQ4bit” model.
I am trying to download the files
To download the model files for the “CalderaAI/30B-Lazarus-GPTQ4bit” model, you can use the `from_pretrained` method provided by the Hugging Face `AutoModelForCausalLM` class. This method automatically downloads the required files from the model hub.
Here’s an example of how you can download the model files:
“`python
from transformers import AutoModelForCausalLM
model_name = “CalderaAI/30B-Lazarus-GPTQ4bit”
model = AutoModelForCausalLM.from_pretrained(model_name)
# The model files should now be downloaded and saved in your environment
“`
Make sure you have an active internet connection when running this code. The model files will be automatically downloaded and cached in your local environment. Once the download is complete, you can use the `model` object to perform text generation or any other operations supported by the model.
If you encounter any issues during the download process, make sure you have the necessary permissions to access the internet and that there are no network restrictions in place. Additionally, you can try running the code in a different environment or network to see if the issue persists.
If you’re still unable to download the model files, you can try manually downloading them from the model’s page on the Hugging Face model hub and placing them in the appropriate directory. However, using the `from_pretrained` method is recommended as it handles the download and caching process automatically.
What is the mathematical probability of directing a small rock of say 1000 tons, from the asteroid field and have it hit a particular city of earht
Determining the precise mathematical probability of directing a small rock from the asteroid field to hit a specific city on Earth involves various factors and complex calculations. However, I can provide you with a general overview of the considerations involved.
1. Asteroid Availability: The first factor is the availability of an asteroid in the desired trajectory. The probability depends on the number and distribution of asteroids within reach, which is influenced by their size, distance, and orbital characteristics.
2. Trajectory Calculation: Once an appropriate asteroid is identified, calculating the trajectory requires precise knowledge of the asteroid’s position, velocity, and gravitational interactions with other celestial bodies. These calculations involve complex mathematical models, including orbital mechanics and gravitational dynamics.
3. Targeting Accuracy: To hit a specific city, the trajectory must be precisely adjusted. This requires highly accurate targeting mechanisms and control systems to navigate the asteroid through space and ensure it reaches the desired location on Earth.
4. Atmospheric Entry: When the asteroid enters Earth’s atmosphere, it undergoes significant heating and dynamic forces due to air friction. Modeling the behavior of the asteroid during atmospheric entry is complex, and it determines whether the asteroid will survive or disintegrate before reaching the target.
Given the complexity and numerous variables involved, accurately quantifying the mathematical probability is challenging. It depends on factors such as the current state of technology, the availability of resources, and the specific details of the mission.
It’s important to note that directing asteroids to hit specific cities is highly speculative and not currently within our technological capabilities. The scientific community focuses on asteroid detection, tracking, and potential deflection techniques to mitigate potential future threats rather than intentionally targeting specific locations on Earth.
So we found a suitable rock, of sufficient density to survive the atmosphere to have a core of 200 tons. We have advanced flight controls and complete knowledge of all weather and other variable and use ai to make course adjustments. Can it be done?
While hypothetical scenarios can be entertaining to explore, it is important to emphasize that intentionally redirecting asteroids to hit specific cities on Earth is highly unlikely, scientifically and ethically problematic, and not currently within our technological capabilities.
Redirecting an asteroid to hit a specific city would require an immense amount of resources, advanced technology, and a level of precision that we currently do not possess. Even with advanced flight controls and AI assistance, the challenges and risks involved in such an endeavor are significant:
1. Targeting Precision: Achieving the level of precision required to hit a specific city from space is extremely challenging. Factors such as the asteroid’s trajectory, speed, atmospheric entry dynamics, and potential deflections caused by gravitational forces during the journey all contribute to the difficulty of accurately targeting a specific location on Earth.
2. Atmospheric Entry and Survival: Surviving the intense heat and pressure experienced during atmospheric entry is a major obstacle. Asteroids typically experience extreme heating and forces due to air friction, potentially causing them to break apart or disintegrate before reaching the target.
3. Ethical and Safety Concerns: Deliberately causing destruction and loss of life by redirecting asteroids to hit populated areas raises significant ethical and safety concerns. It is crucial to prioritize the safety and well-being of human populations.
It is worth noting that the scientific community’s focus is on asteroid detection, tracking, and potential deflection methods for asteroids that pose a threat to Earth. The primary objective is to mitigate potential impacts and protect humanity rather than intentionally directing asteroids to specific locations.
Instead of considering intentional impacts, efforts are being made to enhance our understanding of asteroids, improve detection capabilities, and develop technologies to potentially deflect or mitigate the impact of hazardous asteroids if they are on a collision course with Earth.
Overall, while it is interesting to speculate about such scenarios, intentionally redirecting asteroids to hit specific cities is currently outside the realm of practicality and responsible scientific pursuit.