How to use NVLM only for text task? [wrong pixel_values size: torch.Size([1, 5])]
Getting the error: ValueError: wrong pixel_values size: torch.Size([1, 5])
Following is my code:
I need to use it on CPU
from transformers import AutoModel, AutoTokenizer
import torch
Load model with device map set to "cpu" for CPU usage
path = "nvidia/NVLM-D-72B"
model = AutoModel.from_pretrained(
path,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
use_flash_attn=False,
trust_remote_code=True,
device_map={"": "cpu"} # Ensure everything is on CPU
).eval()
Set device to CPU, since CUDA is not available
device = 'cpu' # Force device to CPU if no GPU available
print(device) # Ensure it prints 'cpu'
model = model.to(device) # Explicitly set the model to CPU
Load tokenizer (no need to move to device)
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
Set up generation configuration
generation_config = dict(max_new_tokens=1024, do_sample=False)
Query for the model
query = 'What is transformer model?'
Tokenize the query
inputs = tokenizer(query, return_tensors="pt").to(device)
Generate response using the model
with torch.no_grad():
outputs = model.generate(inputs["input_ids"], max_length=1024)
Decode and print the response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
Hi @vedantbahel ,
We provide an example in README for text-only gen as follows:
# pure-text conversation
question = 'Hello, who are you?'
response, history = model.chat(tokenizer, None, question, generation_config, history=None, return_history=True)
print(f'User: {question}\nAssistant: {response}')
Please let me know if you have further issues.
Thanks.
Best,
Boxin