CUDA GPTQ Command Line – Dr. Trippers Stuff

I wnat to try this model on the command line: IT MUST BE CUDA. # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained(“TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ”)
model = AutoModelForCausalLM.from_pretrained(“TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ”)
To use the CUDA version of the model on the command line, you can follow these steps:

1. Install the required dependencies:
“`
pip install transformers torch
“`

2. Make sure you have CUDA installed on your machine and that it is compatible with your GPU.

3. Create a new Python file, such as `gpt_cuda.py`, and open it in a text editor.

4. Copy and paste the following code into the file:
“`python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

if torch.cuda.is_available():
device = torch.device(“cuda”)
else:
device = torch.device(“cpu”)

input_text = “Your input text here”

input_ids = tokenizer.encode(input_text, return_tensors=”pt”).to(device)
output = model.generate(input_ids)

generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)
“`

5. Replace `”Your input text here”` with the actual input text you want to use.

6. Save the file.

7. Open a command prompt or terminal and navigate to the directory where you saved the `gpt_cuda.py` file.

8. Run the script using the following command:
“`
python gpt_cuda.py
“`

If your machine has a compatible GPU and CUDA installed, the script will utilize the GPU for acceleration.
Traceback (most recent call last):
File “C:SarahOneTestP38WizardWizard-Vicuna-30B-Uncensored-GPTQapp.py”, line 8, in
model = AutoModelForCausalLM.from_pretrained(“TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ”)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “c:ProgramDataminiconda3envsamberLibsite-packagestransformersmodelsautoauto_factory.py”, line 471, in from_pretrained
return model_class.from_pretrained(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “c:ProgramDataminiconda3envsamberLibsite-packagestransformersmodeling_utils.py”, line 2511, 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
The error message suggests that the model weights for “TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ” are not available in the expected format. This could happen if the model was not properly downloaded or if the model name is incorrect.

Here are a few troubleshooting steps you can try:

1. Double-check the model name: Make sure you have the correct model name `”TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ”`. You can verify the name by checking the model’s documentation or the model repository where you obtained the model.

2. Verify the model files: Confirm that the necessary model files are present in the model’s directory. Look for files like `pytorch_model.bin`, `tf_model.h5`, `model.ckpt`, or `flax_model.msgpack`. If these files are missing, you may need to redownload the model or use a different model.

3. Check the download method: If you manually downloaded the model files, ensure that they are placed in the correct location and that the file paths in your code are accurate.

4. Try a different model: If the issue persists, consider using a different model from the Hugging Face Model Hub. You can browse the available models at https://huggingface.co/models.

If none of these steps resolve the issue, please provide more details about how you obtained the model and any other relevant information.
This is in the config.json file: {
“_name_or_path”: “/workspace/wizard-vicuna-30b-uncensored”,
“architectures”: [
“LlamaForCausalLM”
],
“bos_token_id”: 1,
“eos_token_id”: 2,
“hidden_act”: “silu”,
“hidden_size”: 6656,
“initializer_range”: 0.02,
“intermediate_size”: 17920,
“max_position_embeddings”: 2048,
“max_sequence_length”: 2048,
“model_type”: “llama”,
“num_attention_heads”: 52,
“num_hidden_layers”: 60,
“pad_token_id”: 0,
“rms_norm_eps”: 1e-06,
“tie_word_embeddings”: false,
“torch_dtype”: “float32”,
“transformers_version”: “4.28.1”,
“use_cache”: true,
“vocab_size”: 32000
}

Thank you for providing the `config.json` file for the model. However, the `config.json` alone is not sufficient to load the model. We also need the actual model weights (usually stored in a file named `pytorch_model.bin` or similar).

If you have access to the model weights or the complete model directory, you can try the following steps:

1. Make sure you have the `transformers` library installed:
“`
pip install transformers
“`

2. Create a new Python file, such as `gpt_cuda.py`, and open it in a text editor.

3. Copy and paste the following code into the file:
“`python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

if torch.cuda.is_available():
device = torch.device(“cuda”)
else:
device = torch.device(“cpu”)

tokenizer = AutoTokenizer.from_pretrained(“TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ”)
model = AutoModelForCausalLM.from_pretrained(“/path/to/model/directory”) # Replace with the actual path to the model directory
model.to(device)

input_text = “Your input text here”

input_ids = tokenizer.encode(input_text, return_tensors=”pt”).to(device)
output = model.generate(input_ids)

generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)
“`

4. Replace `”/path/to/model/directory”` with the actual path to the directory where the model weights (`pytorch_model.bin`) and `config.json` are located.

5. Replace `”Your input text here”` with the actual input text you want to use.

6. Save the file.

7. Open a command prompt or terminal and navigate to the directory where you saved the `gpt_cuda.py` file.

8. Run the script using the following command:
“`
python gpt_cuda.py
“`

If you provide the correct path to the model directory and have the necessary model files, this script should load the model and generate text using CUDA if it is available on your machine.