File size: 1,607 Bytes
c80ef98
 
44fbebe
 
c80ef98
44fbebe
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
---
license: apache-2.0
language:
- en
---

This model is finetuend on "mistralai/Mixtral-8x7B-v0.1" with Firefly

## Run the model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name_or_path = 'YeungNLP/firefly-mixtral-8x7b'
max_new_tokens = 500
top_p = 0.9
temperature = 0.35
repetition_penalty = 1.0

model = AutoModelForCausalLM.from_pretrained(
    model_name_or_path,
    trust_remote_code=True,
    low_cpu_mem_usage=True,
    torch_dtype=torch.float16,
    device_map='auto'
)
model = model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)

text = "Compose an engaging travel blog post about a recent trip to Hawaii, highlighting cultural experiences and must-see attractions."

inst_begin_tokens = tokenizer.encode('[INST]', add_special_tokens=False)
inst_end_tokens = tokenizer.encode('[/INST]', add_special_tokens=False)
human_tokens = tokenizer.encode(text, add_special_tokens=False)
input_ids = [tokenizer.bos_token_id] + inst_begin_tokens + human_tokens + inst_end_tokens

# input_ids = human_tokens
input_ids = torch.tensor([input_ids], dtype=torch.long).cuda()

with torch.no_grad():
    outputs = model.generate(
        input_ids=input_ids, max_new_tokens=max_new_tokens, do_sample=True,
        top_p=top_p, temperature=temperature, repetition_penalty=repetition_penalty,
        eos_token_id=tokenizer.eos_token_id
    )
outputs = outputs.tolist()[0][len(input_ids[0]):]
response = tokenizer.decode(outputs)
response = response.strip().replace(tokenizer.eos_token, "").strip()
print("Chatbot:{}".format(response))

```