readme: ---
  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.
  