Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,001 Bytes
9aa0a43 1199506 9aa0a43 1199506 9aa0a43 1199506 9aa0a43 1199506 9aa0a43 1199506 9aa0a43 1199506 9aa0a43 1199506 9aa0a43 1199506 9aa0a43 1199506 9aa0a43 1199506 9aa0a43 1199506 9aa0a43 1199506 9aa0a43 1199506 9aa0a43 1199506 |
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 |
import spaces
import json
import subprocess
from llama_cpp import Llama
from llama_cpp_agent import LlamaCppAgent, MessagesFormatterType
from llama_cpp_agent.providers import LlamaCppPythonProvider
from llama_cpp_agent.chat_history import BasicChatHistory
from llama_cpp_agent.chat_history.messages import Roles
import gradio as gr
from huggingface_hub import hf_hub_download
hf_hub_download(
repo_id="fluently-lm/FluentlyLM-Prinum-Q4_K_M-GGUF",
filename="fluentlylm-prinum-q4_k_m.gguf",
local_dir="./models"
)
@spaces.GPU(duration=90)
def respond(
message,
history: list[tuple[str, str]],
model,
system_message,
max_tokens,
temperature,
top_p,
top_k,
repeat_penalty,
):
chat_template = MessagesFormatterType.GEMMA_2
llm = Llama(
model_path=f"models/{model}",
flash_attn=True,
n_gpu_layers=81,
n_batch=1024,
n_ctx=8192,
)
provider = LlamaCppPythonProvider(llm)
agent = LlamaCppAgent(
provider,
system_prompt=f"{system_message}",
predefined_messages_formatter_type=chat_template,
debug_output=True
)
settings = provider.get_provider_default_settings()
settings.temperature = temperature
settings.top_k = top_k
settings.top_p = top_p
settings.max_tokens = max_tokens
settings.repeat_penalty = repeat_penalty
settings.stream = True
messages = BasicChatHistory()
for msn in history:
user = {
'role': Roles.user,
'content': msn[0]
}
assistant = {
'role': Roles.assistant,
'content': msn[1]
}
messages.add_message(user)
messages.add_message(assistant)
stream = agent.get_chat_response(
message,
llm_sampling_settings=settings,
chat_history=messages,
returns_streaming_generator=True,
print_output=False
)
outputs = ""
for output in stream:
outputs += output
yield outputs
def create_interface(model_name, description):
return gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value=model_name, label="Model", interactive=False),
gr.Textbox(value="You are Fluently, a helpful assistant. You should think step-by-step. You should answer all question by prioritizing the principle of openness", label="System message"),
gr.Slider(minimum=1, maximum=4096, value=2048, step=1, label="Max tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p",
),
gr.Slider(
minimum=0,
maximum=100,
value=40,
step=1,
label="Top-k",
),
gr.Slider(
minimum=0.0,
maximum=2.0,
value=1.1,
step=0.1,
label="Repetition penalty",
),
],
retry_btn="Retry",
undo_btn="Undo",
clear_btn="Clear",
submit_btn="Send",
title=f"{model_name}",
description=description,
examples=[
["Hi! How are you?",
"Write a short story about a scary island.",
"Prove that the force of gravity applies to all bodies in the Universe.",
"Give examples of how a quantum computer works."
],
],
chatbot=gr.Chatbot(
label=None,
scale=1,
likeable=True,
show_copy_button=True
)
)
description = """<h2 align="center"<bold>FluentlyLM Prinum</bold> Demo</h2>"""
interface = create_interface('fluentlylm-prinum-q4_k_m.gguf', description)
demo = gr.Blocks()
with demo:
interface.render()
if __name__ == "__main__":
demo.launch() |