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---
license: apache-2.0
---
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.
"""
``` |