---
license: creativeml-openrail-m
datasets:
- GEM/viggo
metrics:
- accuracy
library_name: transformers
tags:
- 'transformers '
- peft
- qlora
---
```
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

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
)

base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,  # Mistral, same as before
    quantization_config=bnb_config,  # Same quantization config as before
    device_map="auto",
    trust_remote_code=True,
    use_auth_token=True
)

eval_tokenizer = AutoTokenizer.from_pretrained(
    base_model_id,
    add_bos_token=True,
    trust_remote_code=True,
)
```
Now load the QLoRA adapter from the appropriate checkpoint directory
```