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e3d36c9 d62d363 e3d36c9 e71ecba 6bd447e e71ecba d62d363 25612ec d62d363 e3d36c9 d62d363 e3d36c9 25612ec d62d363 e3d36c9 25612ec | 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 | import gradio as gr
from huggingface_hub import hf_hub_download
import subprocess
import sys, platform
from importlib import metadata as md
#Install and Compile wheel at cost of 5minutes
subprocess.run("pip install -V llama_cpp_python==0.3.15", shell=True)
#Add Log to show all versions
print("Python:", platform.python_version(), sys.implementation.name)
print("OS:", platform.uname())
print("\n".join(sorted(f"{d.metadata['Name']}=={d.version}" for d in md.distributions())))
from llama_cpp import Llama
# Download the GGUF model file from the repo
model_repo = "Molchevsky/ai_resume"
model_filename = "merged-Q6_K.gguf"
model_path = hf_hub_download(repo_id=model_repo, filename=model_filename)
# Load the model once (outside the function for efficiency)
# Use chat_format="llama-3" since it's based on Llama 3.2
# Adjust n_ctx if needed for context length
llm = Llama(model_path, chat_format="llama-3", n_ctx=2048)
def respond(
message,
history: list[dict[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
# Extend with history (which is list of dicts with 'role' and 'content')
messages.extend(history)
messages.append({"role": "user", "content": message})
response = ""
# Use create_chat_completion with stream=True
for chunk in llm.create_chat_completion(
messages,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
stream=True,
):
if 'content' in chunk['choices'][0]['delta']:
token = chunk['choices'][0]['delta']['content']
response += token
yield response
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
chatbot = gr.ChatInterface(
respond,
type="messages",
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new 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 (nucleus sampling)",
),
],
)
with gr.Blocks() as demo:
chatbot.render()
if __name__ == "__main__":
demo.launch(debug=True) |