sreeramajay/mistral-samsum-finetune

How to use:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

# Load Base Model
base_model_id = "mistralai/Mistral-7B-v0.1"
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

model = AutoModelForCausalLM.from_pretrained(base_model_id, quantization_config=bnb_config)

eval_tokenizer = AutoTokenizer.from_pretrained(
    base_model_id,
    add_bos_token=True,
    trust_remote_code=True,
)
eval_tokenizer.pad_token = eval_tokenizer.eos_token

# Load Peft Weights
from peft import PeftModel

ft_model = PeftModel.from_pretrained(model, "mistral-samsum-finetune/checkpoint-150")

# Format the Sample Input
def formatting_func(example):
    text = f"### Summarize this dialog:\n{example['dialogue']}\n### Summary:\n{example['summary']}"
    return text

max_length = 256
eval_prompt = {'dialogue': "Amanda: I baked cookies. Do you want some? Jerry: Sure! Amanda: I'll bring you tomorrow :-)",
                              'summary': ''}
eval_prompt = formatting_func(eval_prompt)

# Generate summary for sample Input
model_input = eval_tokenizer(
        eval_prompt,
        truncation=True,
        max_length=max_length,
        padding="max_length",
        return_tensors="pt").to("cuda")

ft_model.eval()
with torch.no_grad():
    print(eval_tokenizer.decode(ft_model.generate(**model_input, 
                                               max_new_tokens=256, 
                                               repetition_penalty=1.15)[0], 
                                skip_special_tokens=True))

# here is the output:
"""
### Summarize this dialog:
Amanda: I baked cookies. Do you want some? Jerry: Sure! Amanda: I'll bring you tomorrow :-)
### Summary:
Jerry will get some cookies from Amanda tomorrow.
"""