from art import *
import time
print(text2art('''TensorLM''', font="small"))
print("Our license: https://www.apache.org/licenses/LICENSE-2.0.txt")
#time.sleep(5)
print(" ")
import os
import gradio as gr
import copy
import llama_cpp
from llama_cpp import Llama
import random
from huggingface_hub import hf_hub_download
#import spaces
#from blip.blip_engine import blip_run
dir = os.getcwd()
def list_models(name):
return os.listdir(f"{dir}/models")
def render_md(text):
return f"{text}"
hf_hub_download(
repo_id="ehristoforu/LLMs",
filename="llama-2-7b-chat.ggmlv3.q2_K.bin",
local_dir="models",
force_download=True, resume_download=False,
cache_dir=".cache",
)
time.sleep(1.5)
llm = Llama(
model_path=f"{dir}/models/llama-2-7b-chat.ggmlv3.q2_K.bin",
n_ctx=1536,
n_gpu_layers=0,
n_threads=16,
verbose=True,
f16_kv=True,
logits_all=False,
vocab_only=False,
use_mmap=True,
use_mlock=False,
n_batch=512,
last_n_tokens_size=64,
low_vram=False,
rope_freq_base=10000,
rope_freq_scale=1,
)
history = []
'''
system_message = """
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
"""
'''
#@spaces.GPU
def generate_text(message, history, system_prompt, preset, temperature, max_tokens, top_p, top_k, repeat_penalty):
temp = ""
input_prompt = f"[INST] <>\nYou are {preset}. {system_prompt}.\n<>\n\n "
for interaction in history:
input_prompt = input_prompt + str(interaction[0]) + " [/INST] " + str(interaction[1]) + " [INST] "
input_prompt = input_prompt + str(message) + " [/INST] "
output = llm(
input_prompt,
temperature=temperature,
top_p=top_p,
top_k=top_k,
repeat_penalty=repeat_penalty,
max_tokens=max_tokens,
stop=[
"<|prompter|>",
"<|endoftext|>",
"<|endoftext|> \n",
"ASSISTANT:",
"USER:",
"SYSTEM:",
],
stream=True,
)
for out in output:
stream = copy.deepcopy(out)
temp += stream["choices"][0]["text"]
yield temp
history = ["init", input_prompt]
chatbot = gr.Chatbot(show_label=False, layout="panel", show_copy_button=True, height=500, min_width=180)
with gr.Blocks(theme="theme-repo/STONE_Theme", title="TensorLM", css="style.css") as demo:
with gr.Row():
model = gr.Dropdown(label="Model (only based on Llama in GGML format (.bin))", choices=os.listdir(f"{dir}/models"), value="llama-2-7b-chat.ggmlv3.q2_K.bin", interactive=False, allow_custom_value=False, scale=50)
#refresh_model = gr.Button(value="Load model", interactive=True, scale=1)
with gr.Row():
with gr.Tab("đŦ"):
with gr.Row(visible=False, render=False) as sliders:
with gr.Tab("Parameters"):
max_tokens = gr.Slider(label="Max new tokens", minimum=256, maximum=4056, value=512, step=8, interactive=True)
temperature = gr.Slider(label="Temperature", minimum=0.01, maximum=2.00, value=0.15, step=0.01, interactive=True)
top_p = gr.Slider(label="Top P", minimum=0.01, maximum=2.00, value=0.10, step=0.01, interactive=True)
top_k = gr.Slider(label="Top K", minimum=10.00, maximum=100.00, value=40.00, step=0.01, interactive=True)
repeat_penalty = gr.Slider(label="Repeat penalty", minimum=0.01, maximum=2.00, value=1.10, step=0.01, interactive=True)
with gr.Tab("Instructions"):
preset = gr.Dropdown(label="Prompt preset", choices=["AI-assistant", "Historical Expert", "Math Tutor", "Python Tutor", "Language Learning Coach", "Philosopher", "Poet"], value="AI-assistant", interactive=True, allow_custom_value=False)
system_prompt = gr.Textbox(label="Custom system prompt", max_lines=4, lines=3, interactive=True)
with gr.Row():
gr.ChatInterface(
generate_text,
chatbot=chatbot,
retry_btn="đī¸",
submit_btn="đ¨",
undo_btn="âŠī¸",
clear_btn="đī¸",
additional_inputs=[system_prompt, preset, temperature, max_tokens, top_k, top_k, repeat_penalty],
autofocus=False,
)
sliders_change = gr.Checkbox(label="Options", value=False, interactive=True)
with gr.Row():
sliders.render()
with gr.Tab("đŊ"):
gr.Markdown("""## Download model from đ¤ HuggingFace.co (DON'T WORK ON HF.space)""")
with gr.Row():
repo_id = gr.Textbox(label="REPO_ID", value="ehristoforu/LLMs", lines=1, max_lines=1, interactive=False)
filename = gr.Dropdown(label="FILENAME", interactive=False, choices=["llama-2-7b-chat.ggmlv3.q2_K.bin", "llama-2-13b-chat.ggmlv3.q2_K.bin", "codellama-7b-instruct.ggmlv3.Q2_K.bin", "codellama-13b-instruct.ggmlv3.Q2_K.bin", "saiga-13b.ggmlv3.Q4_1.bin", "saiga-30b.ggmlv3.Q3_K.bin"], value="", allow_custom_value=False)
download_btn = gr.Button(value="Download", interactive=False)
logs=gr.Markdown()
with gr.Tab("đ"):
with gr.Tab("Notebook"):
with gr.Row():
notebook = gr.Textbox(show_label=False, value="This is a great day...", placeholder="Your notebook", max_lines=40, lines=35, interactive=True)
with gr.Tab("Markdown"):
render_markdown = gr.Button(value="Render markdown from Notebook", interactive=True)
with gr.Row():
markdown = gr.Markdown()
with gr.Tab("âī¸"):
gr.Markdown("## Settings is DON'T WORK ON HF.space.")
with gr.Row():
with gr.Column():
#with gr.Row():
# gr.Markdown("### Style")
# chat_style = gr.Dropdown(label="Style of chat", choices=["bubble", "panel"], value="bubble", interactive=True, allow_custom_value=False)
with gr.Row():
gr.Markdown("### Engine")
reload_model = gr.Button("Apply settings to model", interactive=False)
n_ctx = gr.Slider(label="Number of CTX", minimum=1024, maximum=4056, value=2048, step=8, interactive=True)
n_gpu_layers = gr.Slider(label="Number of GPU layers", minimum=0, maximum=36, value=0, step=1, interactive=True)
n_threads = gr.Slider(label="Number of Threads", minimum=2, maximum=36, value=4, step=1, interactive=True)
verbose = gr.Checkbox(label="Verbose", value=True, interactive=True)
f16_kv = gr.Checkbox(label="F16 KV", value=True, interactive=True)
logits_all = gr.Checkbox(label="Logits all", value=False, interactive=True)
vocab_only = gr.Checkbox(label="Vocab only", value=False, interactive=True)
use_mmap = gr.Checkbox(label="Use mmap", value=True, interactive=True)
use_mlock = gr.Checkbox(label="Use mlock", value=False, interactive=True)
n_batch = gr.Slider(label="Number of batch", minimum=128, maximum=2048, value=512, step=8, interactive=True)
last_n_tokens_size = gr.Slider(label="Last number of tokens size", minimum=8, maximum=512, value=64, step=8, interactive=True)
low_vram = gr.Checkbox(label="Low VRAM", value=False, interactive=True)
rope_freq_base = gr.Slider(label="Rope freq base", minimum=1000.0, maximum=30000.0, value=10000.0, step=0.1, interactive=True)
rope_freq_scale = gr.Slider(label="Rope freq scale", minimum=0.1, maximum=3.0, value=1.0, step=0.1)
with gr.Row():
gr.Markdown("""
gradio 4.1.0 | llama.cpp | python | Suggested models
""")
render_markdown.click(
fn=render_md,
inputs=notebook,
outputs=markdown,
queue=False,
api_name=False,
)
sliders_change.change(
fn=lambda x: gr.update(visible=x),
inputs=sliders_change,
outputs=sliders,
queue=False,
api_name=False,
)
#model.change(load_model, inputs=[model, n_ctx, n_gpu_layers, n_threads, verbose, f16_kv, logits_all, vocab_only, use_mmap, use_mlock, n_batch, last_n_tokens_size, low_vram, rope_freq_base, rope_freq_scale], outputs=model, api_name=False, queue=False)
#reload_model.click(load_model, inputs=[model, n_ctx, n_gpu_layers, n_threads, verbose, f16_kv, logits_all, vocab_only, use_mmap, use_mlock, n_batch, last_n_tokens_size, low_vram, rope_freq_base, rope_freq_scale], outputs=model, api_name=False, queue=True)
demo.queue().launch(
inbrowser=False,
debug=False,
quiet=True,
favicon_path="assets/favicon.png",
)