File size: 4,350 Bytes
51a7d9e
 
 
 
bd34f0b
51a7d9e
edb9e8a
51a7d9e
 
 
566d6bf
1ec2e60
 
51a7d9e
566d6bf
51a7d9e
bd34f0b
 
 
566d6bf
bd34f0b
 
 
 
 
51a7d9e
 
 
bd34f0b
 
 
 
 
 
 
51a7d9e
 
 
 
1ec2e60
bd34f0b
51a7d9e
 
1ec2e60
51a7d9e
 
bd34f0b
fd6304d
 
51a7d9e
 
 
 
 
33e87c8
3b9cb87
bd34f0b
 
3b9cb87
bd34f0b
639e063
edb9e8a
bd34f0b
edb9e8a
bd34f0b
 
 
51a7d9e
 
 
ef2eb9e
51a7d9e
edb9e8a
 
 
51a7d9e
edb9e8a
 
 
 
51a7d9e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ef2eb9e
51a7d9e
 
 
bd34f0b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
51a7d9e
 
 
 
 
 
 
 
 
 
 
16e5a54
51a7d9e
 
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
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
import torch
from PIL import Image
import gradio as gr
import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import os
from threading import Thread


HF_TOKEN = os.environ.get("HF_TOKEN", None)
MODEL_ID = "aixsatoshi/Llama-3-youko-8b-instruct-chatvector"
MODELS = os.environ.get("MODELS")
MODEL_NAME = MODELS.split("/")[-1]

TITLE = "<h1><center>Llama-3-youko-8b-instruct-chatvector</center></h1>"

DESCRIPTION = f"""
<h3>MODEL: <a href="https://hf.co/{MODELS}">{MODEL_NAME}</a></h3>
<center>
<p>youko-8b is the large language model built by  rinna.
<br>
Feel free to test without log.
</p>
</center>
"""

CSS = """
.duplicate-button {
    margin: auto !important;
    color: white !important;
    background: black !important;
    border-radius: 100vh !important;
}
h3 {
    text-align: center;
}
"""

model = AutoModelForCausalLM.from_pretrained(
          MODELS,
          torch_dtype=torch.float16,
          device_map="auto",
        )
tokenizer = AutoTokenizer.from_pretrained(MODELS)

@spaces.GPU
def stream_chat(message: str, history: list, temperature: float, max_new_tokens: int, top_p: float, top_k: int, penalty: float):
    print(f'message is - {message}')
    print(f'history is - {history}')
    conversation = []
    for prompt, answer in history:
        conversation.extend([{"role": "user", "content": prompt}, {"role": "assistant", "content": answer}])
    conversation.append({"role": "user", "content": message})

    #print(f"Conversation is -\n{conversation}")
    
    input_ids = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
    inputs = tokenizer(input_ids, return_tensors="pt").to(0)
    
    streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)

    generate_kwargs = dict(
        inputs, 
        streamer=streamer,
        top_k=top_k,
        top_p=top_p,
        repetition_penalty=penalty,
        max_new_tokens=max_new_tokens, 
        do_sample=True, 
        temperature=temperature,
        eos_token_id = [128001, 128009],
    )
    
    thread = Thread(target=model.generate, kwargs=generate_kwargs)
    thread.start()

    buffer = ""
    for new_text in streamer:
        buffer += new_text
        yield buffer



chatbot = gr.Chatbot(height=450)

with gr.Blocks(css=CSS) as demo:
    gr.HTML(TITLE)
    gr.HTML(DESCRIPTION)
    gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
    gr.ChatInterface(
        fn=stream_chat,
        chatbot=chatbot,
        fill_height=True,
        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
        additional_inputs=[
            gr.Slider(
                minimum=0,
                maximum=1,
                step=0.1,
                value=0.8,
                label="Temperature",
                render=False,
            ),
            gr.Slider(
                minimum=128,
                maximum=4096,
                step=1,
                value=1024,
                label="Max new tokens",
                render=False,
            ),
            gr.Slider(
                minimum=0.0,
                maximum=1.0,
                step=0.1,
                value=0.8,
                label="top_p",
                render=False,
            ),
            gr.Slider(
                minimum=1,
                maximum=20,
                step=1,
                value=20,
                label="top_k",
                render=False,
            ),
            gr.Slider(
                minimum=0.0,
                maximum=2.0,
                step=0.1,
                value=1.0,
                label="Repetition penalty",
                render=False,
            ),
        ],
        examples=[
            ["Help me study vocabulary: write a sentence for me to fill in the blank, and I'll try to pick the correct option."],
            ["What are 5 creative things I could do with my kids' art? I don't want to throw them away, but it's also so much clutter."],
            ["Tell me a random fun fact about the Roman Empire."],
            ["Show me a code snippet of a website's sticky header in CSS and JavaScript."],
        ],
        cache_examples=False,
    )



if __name__ == "__main__":
    demo.launch()