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import os
import shutil
import subprocess
import signal
os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
import gradio as gr
from huggingface_hub import HfApi
from huggingface_hub import ModelCard
from gradio_huggingfacehub_search import HuggingfaceHubSearch
from apscheduler.schedulers.background import BackgroundScheduler
HF_PATH = "https://huggingface.co/"
CONV_TEMPLATES = [
"llama-3",
"llama-3_1",
"chatml",
"chatml_nosystem",
"qwen2",
"open_hermes_mistral",
"neural_hermes_mistral",
"llama_default",
"llama-2",
"mistral_default",
"gpt2",
"codellama_completion",
"codellama_instruct",
"vicuna_v1.1",
"conv_one_shot",
"redpajama_chat",
"rwkv_world",
"rwkv",
"gorilla",
"gorilla-openfunctions-v2",
"guanaco",
"dolly",
"oasst",
"stablelm",
"stablecode_completion",
"stablecode_instruct",
"minigpt",
"moss",
"LM",
"stablelm-3b",
"gpt_bigcode",
"wizardlm_7b",
"wizard_coder_or_math",
"glm",
"custom", # for web-llm only
"phi-2",
"phi-3",
"phi-3-vision",
"stablelm-2",
"gemma_instruction",
"orion",
"llava",
"hermes2_pro_llama3",
"hermes3_llama-3_1",
"tinyllama_v1_0",
"aya-23",
]
QUANTIZATIONS = ["q0f16",
"q0f32",
"q3f16_1",
"q4f16_1",
"q4f32_1",
"q4f16_awq"]
def button_click(hf_model_id, conv_template, quantization, oauth_token: gr.OAuthToken | None):
if not oauth_token.token:
raise ValueError("Log in to Huggingface to use this")
api = HfApi(token=oauth_token.token)
model_dir_name = hf_model_id.split("/")[1]
mlc_model_name = model_dir_name + "-" + quantization + "-" + "MLC"
os.system("mkdir -p dist/models")
os.system("git lfs install")
api.snapshot_download(repo_id=hf_model_id, local_dir=f"./dist/models/{model_dir_name}")
os.system("mlc_llm convert_weight ./dist/models/" + model_dir_name + "/" + \
" --quantization " + quantization + \
" -o dist/" + mlc_model_name)
os.system("mlc_llm gen_config ./dist/models/" + model_dir_name + "/" + \
" --quantization " + quantization + " --conv-template " + conv_template + \
" -o dist/" + mlc_model_name + "/")
# push to HF
user_name = api.whoami()["name"]
api.create_repo(repo_id=f"{user_name}/{mlc_model_name}", private=True)
api.upload_large_folder(folder_path=f"./dist/{mlc_model_name}",
repo_id=f"{user_name}/{mlc_model_name}",
repo_type="model")
os.system("rm -rf dist/")
return "successful"
# demo = gr.Interface(
# fn=button_click,
# inputs = [gr.LoginButton(),
# gr.Textbox(label="HF Model ID"),
# gr.Dropdown(CONV_TEMPLATES, label="Conversation Template"),
# gr.Dropdown(QUANTIZATIONS, label="Quantization Method")],
# outputs = "text"
# )
with gr.Blocks() as demo:
gr.LoginButton()
model_id = gr.Textbox(label="HF Model ID")
conv = gr.Dropdown(CONV_TEMPLATES, label="Conversation Template")
quant = gr.Dropdown(QUANTIZATIONS, label="Quantization Method")
btn = gr.Button("Convert!")
out = gr.Textbox()
btn.click(fn=button_click , inputs=[model_id, conv, quant], outputs=out)
demo.launch() |