# Loaded adapter seems ignored

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**Category:** Beginners\
**Created:** [May 24, 2024, 6:03am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Floaded-adapter-seems-ignored%2F88195 "2024-05-24T06:03:07Z")\
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**Author:** ![Lewox](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/lewox/32/27101_2.png) [@Lewox](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FLewox)%5C
**Post date:** [May 24, 2024, 6:03am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Floaded-adapter-seems-ignored%2F88195%2F1 "2024-05-24T06:03:07Z")

</div>

Hello everyone, I’m an absolute beginner, trying to do some cool stuff.

I have managed to fine-tune a model using QLoRA, used `'NousResearch/Llama-2-7b-chat-hf'` as the base model, and created my own dataset, as a `.jsonl`, with the format `{"text": "<s>[INST] Generate programming exercise of type xxx. [/INST] A programming exercise...</s>"}`, with `trainer.model.save_pretrained(path_adapters, safe_serialization=True)` got the adapter.

I’m trying to load the original model `'NousResearch/Llama-2-7b-chat-hf'` and the adapters and run a query locally, but the results look like they are not taking into consideration the adapter/fine-tuning.

```auto
from peft import PeftModelForCausalLM
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model_path = r'C:\MODEL\Llama-2-7b-chat-hf'
adapter_path = r'C:\MODEL\adapters'
adapter_name = 'adapter_1'
device = 'cpu'

tokenizer = AutoTokenizer.from_pretrained(base_model_path)
tokenizer.pad_token = tokenizer.eos_token

base_model = AutoModelForCausalLM.from_pretrained(
    base_model_path,
    device_map=device,
    low_cpu_mem_usage=True,
)
base_model.config.use_cache = False

adapted_model = PeftModelForCausalLM.from_pretrained(
    base_model,
    adapter_path,
    adapter_name=adapter_name,
    device_map=device
)
print(adapted_model.active_adapter)
# adapted_model.load_adapter(adapter_path, adapter_name=adapter_name)
# adapted_model.set_adapter(adapter_name)
# adapted_model.enable_adapters()

prompt = 'Generate programming exercise of type xxx.'
input_ids = tokenizer(prompt, return_tensors='pt', truncation=True).input_ids
outputs = adapted_model.generate(input_ids=input_ids, max_new_tokens=512, do_sample=False)
output = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(output)

```

Keep in mind that I’m trying to run this with both the model and adapters locally using the CPU.

Calling `enable_adapters` breaks, and not sure if/when to use it, I’ll eventually have several adapters and switch between them, but for now, I want to see results with one.

Thanks in advance.