# Upload a custom model on Hub

**URL:** </static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fupload-a-custom-model-on-hub%2F141182%3E%5C
**Category:** Models\
**Created:** [February 17, 2025, 12:59am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fupload-a-custom-model-on-hub%2F141182 "2025-02-17T00:59:57Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![dgambettavuw](https://avatars.discourse-cdn.com/v4/letter/d/b77776/32.png) [@dgambettavuw](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fdgambettavuw)%5C
**Post date:** [February 17, 2025, 12:59am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fupload-a-custom-model-on-hub%2F141182%2F1 "2025-02-17T00:59:57Z")

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Hi, I have to modify a HF model from my repository changing the forward function, and I do it defining a new class ModifiedModel which takes the original model and defines new forward, and it works well.

The problem is that I don’t understand how to upload completely the new model on a new repo, because if I inherit the push\_to\_hub method from the child model it pushes only the child, so the pushed model is the original one, how can I push the model in order to have online all the new behaviour?

i hope to be clear, thanks…

```auto

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch.nn.functional as F

def reshape_logits(logits):
    sorted_indices = torch.argsort(logits, descending=True) # Ordina i token per probabilità
    original_probs = torch.softmax(logits, dim=-1) # Converti logits in probabilità

    reshaped_probs = torch.zeros_like(original_probs)
    for i, idx in enumerate(sorted_indices):
        reshaped_probs[idx] = 1 / (2 ** (i + 1)) # i+1 per partire da 1

    reshaped_probs = reshaped_probs / reshaped_probs.sum() # Riscala a sommare 1
    reshaped_logits = torch.log(reshaped_probs + 1e-12) # Converti di nuovo in logits

    return reshaped_logits

class ModifiedModel(torch.nn.Module):
    def __init__ (self, base_model):
        super(). __init__ ()
        self.model = base_model

    def forward(self, *args, **kwargs):

        result = self.model.forward(*args, **kwargs)
        last_token_logits = result.logits[0, -1, :]
        probs = F.softmax(last_token_logits, dim=-1)

        reshaped_probs = torch.zeros_like(probs)
        sorted_indices = torch.argsort(probs, descending=True)

        for i, idx in enumerate(sorted_indices):
            reshaped_probs[idx] = 1 / (2 ** (i + 1)) # i+1 per partire da 1
        
        reshaped_probs = reshaped_probs / reshaped_probs.sum() # Riscala a sommare 1
        reshaped_logits = torch.log(reshaped_probs + 1e-12)
        
        result.logits = reshaped_logits.unsqueeze(0).unsqueeze(0)  
          

        return result

# Modello originale e nome per il modello modificato
original_model_name = "dgambettavuw/M_gen5_run0_llama2-7b_wiki_doc1000_real64_synt64_vuw"

# Carica il modello e il tokenizer
model = AutoModelForCausalLM.from_pretrained(original_model_name, torch_dtype=torch.float16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(original_model_name)

modified_model = ModifiedModel(model)

```

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<div class="post-metadata">

**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FJohn6666)%5C
**Post date:** [February 17, 2025, 5:02am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fupload-a-custom-model-on-hub%2F141182%2F2 "2025-02-17T05:02:22Z")

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I’ve never done it, but I think it’s probably something like this…

> **[Building custom models](https://huggingface.co/docs/transformers/main/en/custom_models#sending-the-code-to-the-hub)**
>
> We’re on a journey to advance and democratize artificial intelligence through open source and open science.