Detailed results can be found
here<|im_start|>system
{System}
<|im_end|>
<|im_start|>user
{User}
<|im_end|>
<|im_start|>assistant
{Assistant}
1from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
2from transformers import pipeline
3import torch
4
5model_id = "MaziyarPanahi/calme-2.3-llama3-70b"
6
7model = AutoModelForCausalLM.from_pretrained(
8 model_id,
9 torch_dtype=torch.bfloat16,
10 device_map="auto",
11 trust_remote_code=True,
12 # attn_implementation="flash_attention_2"
13)
14
15tokenizer = AutoTokenizer.from_pretrained(
16 model_id,
17 trust_remote_code=True
18)
19
20streamer = TextStreamer(tokenizer)
21
22pipeline = pipeline(
23 "text-generation",
24 model=model,
25 tokenizer=tokenizer,
26 model_kwargs={"torch_dtype": torch.bfloat16},
27 streamer=streamer
28)
29
30# Then you can use the pipeline to generate text.
31
32messages = [
33 {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
34 {"role": "user", "content": "Who are you?"},
35]
36
37prompt = tokenizer.apply_chat_template(
38 messages,
39 tokenize=False,
40 add_generation_prompt=True
41)
42
43terminators = [
44 tokenizer.eos_token_id,
45 tokenizer.convert_tokens_to_ids("<|im_end|>"),
46 tokenizer.convert_tokens_to_ids("<|eot_id|>") # safer to have this too
47]
48
49outputs = pipeline(
50 prompt,
51 max_new_tokens=2048,
52 eos_token_id=terminators,
53 do_sample=True,
54 temperature=0.6,
55 top_p=0.95,
56)
57print(outputs[0]["generated_text"][len(prompt):])
