import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

device = 'cuda'
model_name = 'robinsmits/Schaapje-2B-Chat-V1.0'

model = AutoModelForCausalLM.from_pretrained(model_name, 
                                             device_map = "auto", 
                                             torch_dtype = torch.bfloat16)

tokenizer = AutoTokenizer.from_pretrained(model_name)

messages = [{"role": "user", "content": "Hoi hoe gaat het ermee?"}]

chat = tokenizer.apply_chat_template(messages, 
                                     tokenize = False, 
                                     add_generation_prompt = True)

input_tokens = tokenizer(chat, return_tensors = "pt").to('cuda')

output = model.generate(**input_tokens, 
                        max_new_tokens = 512,
                        do_sample = True)

output = tokenizer.decode(output[0], skip_special_tokens = False)
print(output)

Intended uses & limitations

As with all LLM's this model can also experience bias and hallucinations. Regardless of how you use this model always perform the necessary testing and validation.

Datasets and Licenses

The following dataset was used for DPO alignment:

BramVanroy/ultra_feedback_dutch_cleaned: apache-2.0

Model Training

The notebook used to train this DPO aligned model is available at the following link: Schaapje-2B-Chat-DPO-V1.0