# Difference between model.generate() and model() outputs
**URL:**
**Author:**  [@Calandula](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FCalandula)%5C
**Post date:** [January 11, 2022, 9:58pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fdifference-between-model-generate-and-model-outputs%2F13589%2F1 "2022-01-11T21:58:54Z")
Hello!
I have been experimenting with transformers library and I wanted to fine-tune GPT-2 with a new custom\_loss function that basically adds the cosine similarity between a certain topic and the generated results (so it is penalizing if the generated phrase has a low cosine similarity with the topic “sports” for example)
Here it is my compute\_loss function
```auto
class GPT2Trainer(Trainer):
def __init__ (self, *args, **kwargs):
super(). __init__ (*args, **kwargs)
def compute_loss(self, model, inputs, return_outputs=False):
# implement custom logic here
custom_loss = super().compute_loss(model, inputs, return_outputs)
outputs = model(**inputs)
"""""""""""""""""""""""
Here I suppose that I will have to decode each generated output in the batch and compute the
mean cosine similarity of all the words with the topic word and then computing the mean over the
batch size
"""""""""""""""""""""""
if type(custom_loss) is tuple:
custom_loss = (custom_loss[0] + (Here I would add the cos similarity), custom_loss[1])
else:
custom_loss = custom_loss + (Here I would add the cos similarity).
return custom_loss
```
Nevertheless, I can’t understand why the results between the function generate(\*\*inputs) and doing the argmax for each one of the logits for the model(\*\*inputs) function are different:
```auto
tokenizer = AutoTokenizer.from_pretrained("gpt2", use_fast=True)
tokenizer.pad_token = tokenizer.eos_token
model = GPT2LMHeadModel.from_pretrained("gpt2")
model.eval()
t = tokenizer.encode("how are you?", return_tensors="pt")
res = model.generate(t)
print(res)
print(tokenizer.decode(res[0], skip_special_tokens=True))
outputs = model(t)
logits = outputs.logits[0]
print(logits.size())
for i in logits:
prob = F.softmax(i)
print(prob)
word_id = prob.argmax()
print(tokenizer.decode(word_id))
```
the result of the first print is “I’m not sure. I’m not sure if I’m going to” but the result of the second one is “, you going”. So I wanted to know what I’m doing wrong or what are the differences between each method in order to know what to use in the compute\_loss function and getting the correct prediction.
TIA
---
**Author:**  [@hwaseem04](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fhwaseem04)%5C
**Post date:** [March 3, 2024, 9:09am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fdifference-between-model-generate-and-model-outputs%2F13589%2F2 "2024-03-03T09:09:58Z")
Facing similar issue. Would love if anyone clarifies it
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**Author:**  [@nielsr](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fnielsr)%5C
**Post date:** [March 3, 2024, 9:27am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fdifference-between-model-generate-and-model-outputs%2F13589%2F3 "2024-03-03T09:27:22Z")
See here: [Generate without using the generate method](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fgenerate-without-using-the-generate-method%2F11379)