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# conda activate audio
# pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu
import llama_cpp
import os
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
huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
hf_hub_download(
repo_id="Qwen/Qwen2-0.5B-Instruct-GGUF",
filename="qwen2-0_5b-instruct-q4_k_m.gguf",
local_dir="./models"
)
hf_hub_download(
repo_id="TobDeBer/gpt2-Q4_K_M-GGUF",
filename="gpt2-q4_k_m.gguf",
local_dir="./models"
)
hf_hub_download(
repo_id="TobDeBer/Meta-Llama-3.1-8B-Instruct-Q4_K_M-GGUF",
filename="meta-llama-3.1-8b-instruct-q4_k_m.gguf",
local_dir="./models",
token=huggingface_token
)
# 5GB
# RichardErkhov/ibm-granite_-_granite-7b-base-gguf
# granite-7b-base.Q4_K_M.gguf
# 4GB
# TobDeBer/granite-8b-code-instruct-128k-Q4_K_M-GGUF
# granite-8b-code-instruct-128k-q4_k_m.gguf
# 5GB
llm = None
llm_model = None
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
global llm
global llm_model
if llm is None or llm_model != model:
llm = Llama(
model_path=f"models/{model}",
flash_attn=True,
n_gpu_layers=81,
n_batch=1024,
n_ctx=8192,
)
llm_model = model
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
description = """<p align="center">Defaults to Qwen 500M<br>
More models in Advanced Section <br></p>
"""
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Dropdown([
'qwen2-0_5b-instruct-q4_k_m.gguf',
'gpt2-q4_k_m.gguf',
'meta-llama-3.1-8b-instruct-q4_k_m.gguf',
],
value="qwen2-0_5b-instruct-q4_k_m.gguf",
label="Model"
),
gr.Textbox(value="You are a helpful assistant.", 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="Chat with Qwen 2 and friends using llama.cpp",
description=description,
chatbot=gr.Chatbot(
scale=1,
likeable=False,
show_copy_button=True
)
)
if __name__ == "__main__":
demo.launch() |