Getting errors when running phi2

I'm getting the following error:

Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Loading checkpoint shards: 100%|██████████| 2/2 [00:02<00:00,  1.15s/it]
Some weights of the model checkpoint at ../compressor/compressor_llm/phi2_dolphin were not used when initializing PhiForCausalLM: ['lm_head.linear.lora_A.default.weight', 'lm_head.linear.lora_B.default.weight']
- This IS expected if you are initializing PhiForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing PhiForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Traceback (most recent call last):
  File "/home/tempus/assistant/pythonProject/llm.py", line 54, in <module>
    original_prompt = create_prompt(system=SYSTEM_MESSAGE, template=PROMPT_TEMPLATE, context_template=CONTEXT_TEMPLATE, user_prompt=prompt_input)
  File "/home/tempus/assistant/pythonProject/llm.py", line 31, in create_prompt
    context = token_compressor.compress_context(user_prompt, context)
  File "/home/tempus/assistant/pythonProject/token_compressor.py", line 16, in compress_context
    compressed_context = compressor.compress_prompt(
  File "/home/tempus/miniconda3/envs/llm/lib/python3.10/site-packages/llmlingua/prompt_compressor.py", line 253, in compress_prompt
    context = self.iterative_compress_prompt(
  File "/home/tempus/miniconda3/envs/llm/lib/python3.10/site-packages/llmlingua/prompt_compressor.py", line 754, in iterative_compress_prompt
    past_key_values = [
TypeError: 'NoneType' object is not iterable

It works just fine with a small amount of tokens (<~350) and throws out this error when I give it some more tokens.
Am I doing it wrong? It doesn't happen with other models

This is my code:

from llmlingua import PromptCompressor

instruction = "summarize the following text. Keep key information. Do not add any additional data and keep the facts accurate."


def compress_context(prompt, context):

    context_list = context.split("\n")
    context_list = ["\n".join(context_list[ii: ii + 4]) for ii in range(0, len(context_list), 4)]

    compressor = PromptCompressor(model_name="../compressor/compressor_llm/phi2_dolphin")

    compressed_context = compressor.compress_prompt(
        context=context_list,
        instruction=instruction,
        question=prompt,
        condition_compare=True,
        condition_in_question='after',
        rank_method='longllmlingua',
        use_sentence_level_filter=False,
        dynamic_context_compression_ratio=0.4,  # enable dynamic_context_compression_ratio
        ratio=0.5,
        concate_question=False
    )

    return compressed_context["compressed_prompt"]

I'm using the dolphin finetune for this