Error using the local llama3.1 model

I tried to load the local model and ran into this issue

Error:

> raise ValueError(
> ValueError: `rope_scaling` must be a dictionary with with two fields, `type` and `factor`, got {'factor': 8.0, 'low_freq_factor': 1.0,
> 'high_freq_factor': 4.0, 'original_max_position_embeddings': 8192, 'rope_type': 'llama3'}

Test code：
```
from transformers import AutoTokenizer
from petals import AutoDistributedModelForCausalLM

# Choose any model available at https://health.petals.dev
model_name = "./ckpt/Meta-Llama-3.1-405B-Instruct"

# Connect to a distributed network hosting model layers
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoDistributedModelForCausalLM.from_pretrained(model_name)

# Run the model as if it were on your computer
inputs = tokenizer("A cat sat", return_tensors="pt")["input_ids"]
outputs = model.generate(inputs, max_new_tokens=5)
print(tokenizer.decode(outputs[0])) # A cat sat on a mat...
```

The current problem is that transformers are in the wrong version, 4.33.3 should be used to solve this problem, but 4.33.3 is not compatible with petals.

petals:2.2.0.post1