Llama-3-8B-tr / tabbed.py
umarigan's picture
Update tabbed.py
f2f5944 verified
import gradio as gr
import yaml
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import LocalEntryNotFoundError
from llama_cpp import Llama
with open("./config.yml", "r") as f:
config = yaml.load(f, Loader=yaml.Loader)
while True:
try:
load_config = config.copy()
hub_config = load_config["hub"].copy()
repo_id = hub_config.pop("repo_id")
filename = hub_config.pop("filename")
fp = hf_hub_download(
repo_id=repo_id, filename=filename, **hub_config
)
break
except LocalEntryNotFoundError as e:
if "Connection error" in str(e):
print(str(e) + ", retrying...")
else:
raise(e)
llm = Llama(model_path=fp, **config["llama_cpp"])
def user(message, history):
history = history or []
# Append the user's message to the conversation history
history.append([message, ""])
return "", history
def chat(history, system_message, max_tokens, temperature, top_p, top_k, repeat_penalty):
history = history or []
messages = system_message.strip() + "\n" + \
"\n".join(["\n".join(["Kullanıcı: "+item[0], "Asistan: "+item[1]])
for item in history])
# remove last space from assistant, some models output a ZWSP if you leave a space
messages = messages[:-1]
history[-1][1] = ""
for output in llm(
messages,
echo=False,
stream=True,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
top_k=top_k,
repeat_penalty=repeat_penalty,
**config['chat']
):
answer = output['choices'][0]['text']
history[-1][1] += answer
# stream the response
yield history, history
def rp_chat(history, system_message, max_tokens, temperature, top_p, top_k, repeat_penalty):
history = history or []
messages = "<|system|>" + system_message.strip() + "\n" + \
"\n".join(["\n".join(["<|user|>"+item[0], "<|model|>"+item[1]])
for item in history])
# remove last space from assistant, some models output a ZWSP if you leave a space
messages = messages[:-1]
history[-1][1] = ""
for output in llm(
messages,
echo=False,
stream=True,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
top_k=top_k,
repeat_penalty=repeat_penalty,
**config['chat']
):
answer = output['choices'][0]['text']
history[-1][1] += answer
# stream the response
yield history, history
def clear_chat(chat_history_state, chat_message):
chat_history_state = []
chat_message = ''
return chat_history_state, chat_message
start_message = """
- Akıllı, dürüst ve yardımsever bir asistansın.
- Her türlü soruya dürüstçe cevap vereceksin.
"""
def generate_text_instruct(input_text):
response = ""
for output in llm(f"Kullanıcı: {input_text}\nAsistan:", echo=False, stream=True, **config['chat']):
answer = output['choices'][0]['text']
response += answer
yield response
instruct_interface = gr.Interface(
fn=generate_text_instruct,
inputs=gr.inputs.Textbox(lines= 10, label="Enter your input text"),
outputs=gr.outputs.Textbox(label="Output text"),
)
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
gr.Markdown(f"""
### This is the [{config["hub"]["repo_id"]}](https://huggingface.co./{config["hub"]["repo_id"]}) quantized model file [{config["hub"]["filename"]}](https://huggingface.co./{config["hub"]["repo_id"]}/blob/main/{config["hub"]["filename"]})
<details>
<summary><a href="https://huggingface.co./spaces/Nekochu/Luminia-13B-v3-GGUF?duplicate=true">Duplicate the Space</a> to skip the queue and run in a private space or to use your own GGUF models, simply update the <a href="https://huggingface.co./spaces/Nekochu/Luminia-13B-v3-GGUF/blob/main/config.yml">config.yml</a></summary>
<ul>
<li>This Space uses GGUF with CPU-<strong>FREE</strong>, GPU support, so it can quickly run larger models on smaller GPUs & VRAM. <a href="https://github.com/OpenAccess-AI-Collective/ggml-webui">[Contribute]</a></li>
<li>This is running on a smaller, shared GPU, so it may take a few seconds to respond.</li>
</ul>
</details>
""")
with gr.Tab("Chatbot"):
gr.Markdown("# GGUF Spaces Chatbot Demo")
chatbot = gr.Chatbot()
with gr.Row():
message = gr.Textbox(
label="Ne konuda konuşmak istersin?",
placeholder="Bana bir şeyler sor.",
lines=3,
)
with gr.Row():
submit = gr.Button(value="Mesaj Gönder", variant="secondary").style(full_width=True)
roleplay = gr.Button(value="Rol", variant="secondary").style(full_width=True)
clear = gr.Button(value="Yeni Konu", variant="secondary").style(full_width=False)
stop = gr.Button(value="Dur", variant="secondary").style(full_width=False)
with gr.Row():
with gr.Column():
max_tokens = gr.Slider(20, 1000, label="Max Tokens", step=20, value=300)
temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=0.8)
top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.95)
top_k = gr.Slider(0, 100, label="Top K", step=1, value=40)
repeat_penalty = gr.Slider(0.0, 2.0, label="Repetition Penalty", step=0.1, value=1.1)
system_msg = gr.Textbox(
start_message, label="System Message", interactive=True, visible=True, placeholder="system prompt, useful for RP", lines=5)
chat_history_state = gr.State()
clear.click(clear_chat, inputs=[chat_history_state, message], outputs=[chat_history_state, message], queue=False)
clear.click(lambda: None, None, chatbot, queue=False)
submit_click_event = submit.click(
fn=user, inputs=[message, chat_history_state], outputs=[message, chat_history_state], queue=True
).then(
fn=chat, inputs=[chat_history_state, system_msg, max_tokens, temperature, top_p, top_k, repeat_penalty], outputs=[chatbot, chat_history_state], queue=True
)
roleplay_click_event = roleplay.click(
fn=user, inputs=[message, chat_history_state], outputs=[message, chat_history_state], queue=True
).then(
fn=rp_chat, inputs=[chat_history_state, system_msg, max_tokens, temperature, top_p, top_k, repeat_penalty], outputs=[chatbot, chat_history_state], queue=True
)
# message_submit_event = message.submit(
# fn=user, inputs=[message, chat_history_state], outputs=[message, chat_history_state], queue=True
# ).then(
# fn=chat, inputs=[chat_history_state, system_msg, max_tokens, temperature, top_p, top_k, repeat_penalty], outputs=[chatbot, chat_history_state], queue=True
# )
stop.click(fn=None, inputs=None, outputs=None, cancels=[submit_click_event, roleplay_click_event], queue=False)
with gr.Tab("Instruct"):
gr.Markdown("# GGUF Spaces Instruct Demo")
instruct_interface.render()
demo.queue(**config["queue"]).launch(debug=True, server_name="0.0.0.0", server_port=7860)