metadata

library_name: transformers
tags: []

Model Card for Model ID

How to Get Started with the Model

To load and use this model with the Transformers library by Hugging Face, follow the steps outlined in the code snippet below. This code demonstrates how to configure the model, load it along with its tokenizer, and perform inference to generate text based on a given prompt.

Code Format:

from peft import PeftModel, PeftConfig

test_config = PeftConfig.from_pretrained("checkiejan/phi2-marking-test-full")
model_base = AutoModelForCausalLM.from_pretrained(
    test_config.base_model_name_or_path,
    device_map="auto",
    quantization_config=BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=torch.bfloat16,
        bnb_4bit_quant_type="nf4",
    ),
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(test_config.base_model_name_or_path)

# Add/set tokens same tokens to base model before merging, like we did before starting training  
tokenizer.add_tokens(["<|im_start|>", "<PAD>"])
tokenizer.pad_token = "<PAD>"
tokenizer.add_special_tokens(dict(eos_token="<|im_end|>"))
model_base.resize_token_embeddings(
    new_num_tokens=len(tokenizer),
    pad_to_multiple_of=64)   # phi2 default is 64, see configuration_phi.py
model_base.config.eos_token_id = tokenizer.eos_token_id
lora_model = PeftModel.from_pretrained(model_base, "checkiejan/phi2-marking-test-full")

inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=True)
outputs = lora_model.generate(**inputs)
text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
print(''.join(text))

This code snippet sets up the model and tokenizer, configures the necessary parameters, and demonstrates how to generate text based on a given prompt. Ensure to replace "Your prompt here" with your actual input text.

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How to Get Started with the Model

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