import os import shutil import subprocess import gradio as gr from huggingface_hub import create_repo, HfApi from huggingface_hub import snapshot_download from huggingface_hub import whoami from huggingface_hub import ModelCard from textwrap import dedent api = HfApi() def process_model(model_id, q_method, hf_token): MODEL_NAME = model_id.split('/')[-1] fp16 = f"{MODEL_NAME}/{MODEL_NAME.lower()}.fp16.bin" username = whoami(hf_token)["name"] snapshot_download(repo_id=model_id, local_dir = f"{MODEL_NAME}", local_dir_use_symlinks=False) print("Model downloaded successully!") fp16_conversion = f"python llama.cpp/convert.py {MODEL_NAME} --outtype f16 --outfile {fp16}" subprocess.run(fp16_conversion, shell=True) print("Model converted to fp16 successully!") qtype = f"{MODEL_NAME}/{MODEL_NAME.lower()}.{q_method.upper()}.gguf" quantise_ggml = f"./llama.cpp/quantize {fp16} {qtype} {q_method}" subprocess.run(quantise_ggml, shell=True) print("Quantised successfully!") # Create empty repo repo_id = f"{username}/{MODEL_NAME}-{q_method}-GGUF" repo_url = create_repo( repo_id = repo_id, repo_type="model", exist_ok=True, token=hf_token ) print("Empty repo created successfully!") card = ModelCard.load(model_id) card.data.tags = ["llama-cpp"] if card.data.tags is None else card.data.tags + ["llama-cpp"] card.text = dedent( f""" # {repo_id} This model was converted to GGUF format from [`{model_id}`](https://huggingface.co/{model_id}) using llama.cpp. Refer to the [original model card](https://huggingface.co/{model_id}) for more details on the model. ## Use with llama.cpp ```bash brew install ggerganov/ggerganov/llama.cpp ``` ```bash llama-cli --hf-repo {repo_id} --model {qtype.split("/")[-1]} -p "The meaning to life and the universe is " ``` """ ) card.save(os.path.join(MODEL_NAME, "README-new.md")) api.upload_file( path_or_fileobj=qtype, path_in_repo=qtype.split("/")[-1], repo_id=repo_id, repo_type="model", ) api.upload_file( path_or_fileobj=f"{MODEL_NAME}/README-new.md", path_in_repo="README.md", repo_id=repo_id, repo_type="model", ) print("Uploaded successfully!") shutil.rmtree(MODEL_NAME) print("Folder cleaned up successfully!") return ( f'Find your repo here', "llama.png", ) # Create Gradio interface iface = gr.Interface( fn=process_model, inputs=[ gr.Textbox( lines=1, label="Hub Model ID", info="Model repo ID" ), gr.Dropdown( ["Q2_K", "Q3_K_S", "Q3_K_M", "Q3_K_L", "Q4_0", "Q4_K_S", "Q4_K_M", "Q5_0", "Q5_K_S", "Q5_K_M", "Q6_K", "Q8_0"], label="Quantization Method", info="GGML quantisation type" ), gr.Textbox( lines=1, label="HF Write Token", info="https://hf.co/settings/token" ) ], outputs=[ gr.Markdown(label="output"), gr.Image(show_label=False), ], title="Create your own GGUF Quants!", description="Create GGUF quants from any Hugging Face repository! You need to specify a write token obtained in https://hf.co/settings/tokens.", article="

Find your write token at token settings

", ) # Launch the interface iface.launch(debug=True)