# Use model after training

Once you have trained a model using either the SFTTrainer or DPOTrainer, you will have a fine-tuned model that can be used for text generation. In this section, we'll walk through the process of loading the fine-tuned model and generating text. If you need to run an inference server with the trained model, you can explore libraries such as [`text-generation-inference`](https://github.com/huggingface/text-generation-inference).

## Load and Generate

If you have fine-tuned a model fully, meaning without the use of PEFT you can simply load it like any other language model in transformers:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name_or_path = "Qwen/Qwen3-0.6B" #path/to/your/model/or/name/on/hub
device = "cpu" # or "cuda" if you have a GPU

model = AutoModelForCausalLM.from_pretrained(model_name_or_path).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)

inputs = tokenizer.encode("This movie was really", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```

Alternatively you can also use the pipeline:

```python
from transformers import pipeline

model_name_or_path = "Qwen/Qwen3-0.6B" #path/to/your/model/or/name/on/hub
pipe = pipeline("text-generation", model=model_name_or_path)
print(pipe("This movie was really")[0]["generated_text"])
```

## Use Adapters PEFT

```python
from peft import PeftConfig, PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model_name = "Qwen/Qwen3-0.6B" #path/to/your/model/or/name/on/hub
adapter_model_name = "path/to/my/adapter"

model = AutoModelForCausalLM.from_pretrained(base_model_name)
model = PeftModel.from_pretrained(model, adapter_model_name)

tokenizer = AutoTokenizer.from_pretrained(base_model_name)
```

You can also merge the adapters into the base model so you can use the model like a normal transformers model, however the checkpoint will be significantly bigger:

```python
model = AutoModelForCausalLM.from_pretrained(base_model_name)
model = PeftModel.from_pretrained(model, adapter_model_name)

model = model.merge_and_unload()
model.save_pretrained("merged_adapters")
```

Once you have the model loaded and either merged the adapters or keep them separately on top you can run generation as with a normal model outlined above.