Spaces:
Paused
Paused
File size: 4,305 Bytes
927b5de a8cea35 927b5de |
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 |
from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM
from peft import PeftModel, PeftConfig
import torch
import gradio as gr
import random
from textwrap import wrap
EXAMPLES = [
["Hey Falcon! Any recommendations for my holidays in Abu Dhabi?"],
["What's the Everett interpretation of quantum mechanics?"],
["Give me a list of the top 10 dive sites you would recommend around the world."],
["Can you tell me more about deep-water soloing?"],
["Can you write a short tweet about the release of our latest AI model, Falcon LLM?"]
]
device = "cuda" if torch.cuda.is_available() else "cpu"
base_model_id = "tiiuae/falcon-7b-instruct"
model_directory = "Tonic/GaiaMiniMed"
tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True, padding_side="left")
model_config = AutoConfig.from_pretrained(base_model_id)
peft_model = AutoModelForCausalLM.from_pretrained(model_directory, config=model_config)
peft_model = PeftModel.from_pretrained(peft_model, model_directory)
def format_prompt(message, history, system_prompt):
prompt = ""
if system_prompt:
prompt += f"System: {system_prompt}\n"
for user_prompt, bot_response in history:
prompt += f"User: {user_prompt}\n"
prompt += f"Falcon: {bot_response}\n" # Response already contains "Falcon: "
prompt += f"""User: {message}
Falcon:"""
return prompt
seed = 42
def generate(
prompt, history, system_prompt="", temperature=0.9, max_new_tokens=500, top_p=0.95, repetition_penalty=1.0,
):
temperature = float(temperature)
if temperature < 1e-2:
temperature = 1e-2
top_p = float(top_p)
global seed
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=1.0,
stop_sequences="[END]",
do_sample=True,
seed=seed,
)
seed = seed + 1
formatted_prompt = format_prompt(prompt, history, system_prompt)
try:
stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream:
output += response.token.text
for stop_str in STOP_SEQUENCES:
if output.endswith(stop_str):
output = output[:-len(stop_str)]
output = output.rstrip()
yield output
yield output
except Exception as e:
raise gr.Error(f"Error while generating: {e}")
return output
additional_inputs=[
gr.Textbox("", label="Optional system prompt"),
gr.Slider(
label="Temperature",
value=0.9,
minimum=0.0,
maximum=1.0,
step=0.05,
interactive=True,
info="Higher values produce more diverse outputs",
),
gr.Slider(
label="Max new tokens",
value=256,
minimum=0,
maximum=3000,
step=64,
interactive=True,
info="The maximum numbers of new tokens",
),
gr.Slider(
label="Top-p (nucleus sampling)",
value=0.90,
minimum=0.01,
maximum=0.99,
step=0.05,
interactive=True,
info="Higher values sample more low-probability tokens",
),
gr.Slider(
label="Repetition penalty",
value=1.2,
minimum=1.0,
maximum=2.0,
step=0.05,
interactive=True,
info="Penalize repeated tokens",
)
]
with gr.Blocks() as demo:
with gr.Row():
with gr.Column(scale=0.4):
gr.Image("better_banner.jpeg", elem_id="banner-image", show_label=False)
with gr.Column():
gr.Markdown(
# 👋🏻Welcome to Tonic's GaiaMiniMed Chat🚀"
"You can use this Space to test out the current model [(Tonic/GaiaMiniMed)](https://huggingface.co/Tonic/GaiaMiniMed) or duplicate this Space and use it locally or on 🤗HuggingFace. [Join me on Discord to build together](https://discord.gg/VqTxc76K3u)."
)
gr.ChatInterface(
generate,
examples=EXAMPLES,
additional_inputs=additional_inputs,
)
demo.queue(concurrency_count=100, api_open=False).launch(show_api=False)
|