transformers.LlamaTokenizer.from_pretrained does not work after transformers are packaged with Pyinstaller
When I use the following code in my python project, the model will load and work properly, but when I package my project using Pyinstaller, I found by debugging that this code to load the model, does not work properly: tokenizer = transformers.LlamaTokenizer.from_ pretrained(LLM_CHECKPOINT). This complete function is shown below, where LLM_CHECKPOINT = 'D:/LLM/alpaca-llama-7b-fp16' and device = torch.device('cpu'):
import torch
import transformers

def llm_initial(LLM_CHECKPOINT, device):
    tokenizer = transformers.LlamaTokenizer.from_pretrained(LLM_CHECKPOINT)
    model = transformers.LlamaForCausalLM.from_pretrained(LLM_CHECKPOINT).to(device)
    model.eval()

    generation_config = transformers.GenerationConfig(
        # max_new_tokens: This is the maximum number of tokens to generate.
        # The generated sequence will not exceed this length.
        max_new_tokens=256,
        # temperature: This is a parameter for controlling the randomness of predictions
        # by scaling the logits before applying softmax. Higher values (greater than 1)
        # increase randomness, while lower values make the model more deterministic.
        temperature=1,
        # top_k: This parameter controls the number of highest probability predictions
        # to consider for the next token. It's used to introduce some randomness and
        # creativity into the model's outputs.
        top_k=40,
        # top_p: This parameter is also known as nucleus sampling and is used to ensure
        # that the cumulative probability of the considered tokens is at least top_p.
        # This parameter also introduces randomness into the model's outputs.
        top_p=0.9,
        # repetition_penalty: This parameter is used to control for repetitive behavior
        # in the model's outputs. If the value is greater than 1.0, the model is
        # penalized for generating the same token repeatedly.
        repetition_penalty=1.15
    )
    return [device, tokenizer, model, generation_config]


def llm_response(device, tokenizer, model, generation_config, prompt):
    prompt = generate_prompt(prompt)
    input_ids = tokenizer.encode(prompt, return_tensors='pt').to(device)

    with torch.enable_grad():
        output_ids = model.generate(input_ids=input_ids, generation_config=generation_config)

    LLM_RESPONSE = tokenizer.decode(output_ids[0], skip_special_tokens=True)
    return LLM_RESPONSE.replace(prompt, '').strip()
The Pypi package does not have access to your local drive where `LLM_CHECKPOINT` points to, that's why you get different behavior.<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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