---
license: afl-3.0
language:
- en
widget:
- text: "<question>What's my name?<answer>"
  example_title: "Who am I?"
- text: "<question>How to make a campfire<answer>"
  example_title: "Tutorial"
---

# Supervised Finetuning demonstration

Models are finetuned on generated conversation curated from the [Open Assistant](https://github.com/LAION-AI/Open-Assistant).


# Mixing reward model with sampling

We can use reward model to rank the best answer using this example code:

```
import torch
from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("facebook/galactica-1.3b-base-finetuned/checkpoint-1000")
model = AutoModelForCausalLM.from_pretrained("facebook/galactica-1.3b-base-finetuned/checkpoint-1000").eval().half().cuda()

reward_name = "theblackcat102/electra-large-reward-model"
rank_model, rank_tokenizer = AutoModelForSequenceClassification.from_pretrained(reward_name), AutoTokenizer.from_pretrained(reward_name)
rank_model = rank_model.eval().half().cuda()

questions = ["<question>How do I make a resume?<answer>"]
for question in questions:
    inputs = tokenizer(question, return_tensors="pt", padding=True).to(0)
    if 'token_type_ids' in inputs:
        inputs.pop('token_type_ids')
    outputs = model.generate(**inputs, do_sample=True,
        top_k=60,
        max_length=220,
        num_return_sequences=80, 
        early_stopping=True
    )
    print(question)

    results = []
    for i, beam_output in enumerate(outputs):
        output = tokenizer.decode(beam_output, truncate_before_pattern=[r"\n\n^#", "^'''", "\n\n\n"])
        question, answer = output.split('<answer>', maxsplit=1)
        answer = answer.split('<question>')[0].replace('<|endoftext|>', '').lstrip().split('<answer>')[0]
        rank_inputs = rank_tokenizer(question, answer, return_tensors="pt", padding=True, max_length=512, truncation=True).to(1)
        score = rank_model(**rank_inputs).logits[0].cpu().detach()
        results.append((answer, score, output))
    full_results[question] = results
    sorted_result = sorted(results, key=lambda x:x[1], reverse=True)
    total_scores += sorted_result[0][1].item()
    print('score',sorted_result[0][1].item())
    print('-----Best rank-----')
    print(sorted_result[0][0])
    print('-------------------')
```


Checkout weights and biases [report](https://api.wandb.ai/report/theblackcat102/8yg0c0r2) for training detail. 

Thanks to [BASIC lab](https://basiclab.lab.nycu.edu.tw/Yummy/index.html#) for compute resource. BASIC Lab is an academic research lab which focuses in multi-modality learning and data mining domain.