readme: Galactica-6.7b finetuned on webgpt and prompt_dialogue.
  
  Demo use:
  
  ```
  import torch
  from torch import nn
  from torch.nn import functional as F
  import transformers
  
  base_path = 'sanagnos/galactica-6.7b-finetuned'
  
  model = transformers.OPTForCausalLM.from_pretrained(
      base_path, load_in_8bit=True, device_map='auto', low_cpu_mem_usage=True,
      torch_dtype=torch.float16, offload_state_dict=True
  )
  
  model.gradient_checkpointing_enable()  # reduce number of stored activations
  model.model.decoder.project_in = lambda x: x.requires_grad_(True)
  
  class CastOutputToFloat(nn.Sequential):
      def forward(self, x): return super().forward(x).to(torch.float32)
  
  model.lm_head = CastOutputToFloat(model.lm_head)
  
  tokenizer = transformers.AutoTokenizer.from_pretrained(base_path)
  
  batch = tokenizer.encode("<question>What are the symptoms of Alzheimer's disease?<answer>", return_tensors="pt")
  
  with torch.cuda.amp.autocast():
      out = model.generate(
          input_ids=batch.to(model.device),
          max_length=300,
          do_sample=True,
          top_k=40,
          num_beams=1,
          num_return_sequences=1,
          eos_token_id=tokenizer.additional_special_tokens_ids[tokenizer.additional_special_tokens.index('<question>')]
      )
  
  print(tokenizer.decode(out[0, :-1]).replace('<question>', "User:\n").replace('<answer>', '\nAssistant:\n'))
  ```