1from transformers import AutoTokenizer, AutoModelForCausalLM
2import torch
3
4model_id = "li11111/Mistral-7B-Base-RSPO"
5
6tokenizer = AutoTokenizer.from_pretrained(model_id)
7model = AutoModelForCausalLM.from_pretrained(
8 model_id,
9 torch_dtype=torch.bfloat16,
10 device_map="auto",
11)
12
13messages = [
14 {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
15 {"role": "user", "content": "Who are you?"},
16]
17
18input_ids = tokenizer.apply_chat_template(
19 messages,
20 add_generation_prompt=True,
21 return_tensors="pt"
22).to(model.device)
23
24terminators = [
25 tokenizer.eos_token_id
26]
27
28outputs = model.generate(
29 input_ids,
30 max_new_tokens=256,
31 eos_token_id=terminators,
32 do_sample=True,
33 temperature=0.6,
34 top_p=0.9,
35)
36response = outputs[0][input_ids.shape[-1]:]
37print(tokenizer.decode(response, skip_special_tokens=True))
We use the
HuggingFaceH4/ultrafeedback_binarized dataset to train the Mistral Base model.
