globecen commited on
Commit
3528272
1 Parent(s): ad76704

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +14 -65
app.py CHANGED
@@ -1,10 +1,7 @@
1
  from huggingface_hub import InferenceClient
2
  import gradio as gr
3
 
4
- client = InferenceClient(
5
- "mistralai/Mixtral-8x7B-Instruct-v0.1"
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- )
7
-
8
 
9
  def format_prompt(message, history):
10
  prompt = "<s>"
@@ -15,7 +12,7 @@ def format_prompt(message, history):
15
  return prompt
16
 
17
  def generate(
18
- prompt, history, system_prompt, temperature=0.9, max_new_tokens=1024, top_p=0.95, repetition_penalty=1.0,
19
  ):
20
  temperature = float(temperature)
21
  if temperature < 1e-2:
@@ -31,7 +28,8 @@ def generate(
31
  seed=42,
32
  )
33
 
34
- formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
 
35
  stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
36
  output = ""
37
 
@@ -40,64 +38,15 @@ def generate(
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  yield output
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  return output
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43
 
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- additional_inputs=[
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- gr.Textbox(
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- label="System Prompt",
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- max_lines=1,
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- interactive=True,
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- ),
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- gr.Slider(
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- label="Temperature",
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- value=0.9,
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- minimum=0.0,
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- maximum=1.0,
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- step=0.05,
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- interactive=True,
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- info="Higher values produce more diverse outputs",
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- ),
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- gr.Slider(
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- label="Max new tokens",
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- value=256,
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- minimum=0,
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- maximum=1048,
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- step=64,
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- interactive=True,
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- info="The maximum numbers of new tokens",
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- ),
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- gr.Slider(
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- label="Top-p (nucleus sampling)",
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- value=0.90,
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- minimum=0.0,
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- maximum=1,
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- step=0.05,
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- interactive=True,
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- info="Higher values sample more low-probability tokens",
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- ),
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- gr.Slider(
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- label="Repetition penalty",
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- value=1.2,
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- minimum=1.0,
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- maximum=2.0,
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- step=0.05,
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- interactive=True,
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- info="Penalize repeated tokens",
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- )
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- ]
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-
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- examples=[["I'm planning a vacation to Japan. Can you suggest a one-week itinerary including must-visit places and local cuisines to try?", None, None, None, None, None, ],
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- ["Can you write a short story about a time-traveling detective who solves historical mysteries?", None, None, None, None, None,],
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- ["I'm trying to learn French. Can you provide some common phrases that would be useful for a beginner, along with their pronunciations?", None, None, None, None, None,],
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- ["I have chicken, rice, and bell peppers in my kitchen. Can you suggest an easy recipe I can make with these ingredients?", None, None, None, None, None,],
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- ["Can you explain how the QuickSort algorithm works and provide a Python implementation?", None, None, None, None, None,],
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- ["What are some unique features of Rust that make it stand out compared to other systems programming languages like C++?", None, None, None, None, None,],
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- ]
95
 
96
- gr.ChatInterface(
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- fn=generate,
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- chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
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- additional_inputs=additional_inputs,
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- title="globecen bot",
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- examples=examples,
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- concurrency_limit=20,
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- ).launch(show_api=False)
 
1
  from huggingface_hub import InferenceClient
2
  import gradio as gr
3
 
4
+ client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
 
 
 
5
 
6
  def format_prompt(message, history):
7
  prompt = "<s>"
 
12
  return prompt
13
 
14
  def generate(
15
+ prompt, history, temperature=0.2, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
16
  ):
17
  temperature = float(temperature)
18
  if temperature < 1e-2:
 
28
  seed=42,
29
  )
30
 
31
+ formatted_prompt = format_prompt(prompt, history)
32
+
33
  stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
34
  output = ""
35
 
 
38
  yield output
39
  return output
40
 
41
+
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+ mychatbot = gr.Chatbot(
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+ avatar_images=["./user.png", "./botm.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,)
44
 
45
+ demo = gr.ChatInterface(fn=generate,
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+ chatbot=mychatbot,
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+ title="globecen Mixtral 8x7b Chat",
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+ retry_btn=None,
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+ undo_btn=None
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+ )
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ demo.queue().launch(show_api=False)