rm_old

gguf-imat.py

@@ -1,157 +0,0 @@
-import os
-import requests
-import zipfile
-import subprocess
-import shutil
-from huggingface_hub import snapshot_download
-
-# Function to clone or update the llama.cpp repository with shallow cloning
-def clone_or_update_llama_cpp():
-    print("Preparing...")
-    base_dir = os.path.dirname(os.path.abspath(__file__))
-    os.chdir(base_dir)  # Move to the base directory of the script
-    if not os.path.exists("llama.cpp"):
-        subprocess.run(["git", "clone", "--depth", "1", "https://github.com/ggerganov/llama.cpp"])
-    else:
-        os.chdir("llama.cpp")
-        subprocess.run(["git", "pull"])
-    os.chdir(base_dir)  # Move back to the base directory
-    print("The 'llama.cpp' repository is ready.")
-
-# Function to download and extract the latest release of llama.cpp
-def download_llama_release():
-    base_dir = os.path.dirname(os.path.abspath(__file__))
-    dl_dir = os.path.join(base_dir, "bin", "dl")
-    if not os.path.exists(dl_dir):
-        os.makedirs(dl_dir)
-
-    os.chdir(dl_dir)
-    latest_release_url = "https://github.com/ggerganov/llama.cpp/releases/latest"
-    response = requests.get(latest_release_url)
-    if response.status_code == 200:
-        latest_release_tag = response.url.split("/")[-1]
-        download_url = f"https://github.com/ggerganov/llama.cpp/releases/download/{latest_release_tag}/llama-{latest_release_tag}-bin-win-cublas-cu12.2.0-x64.zip"
-        response = requests.get(download_url)
-        if response.status_code == 200:
-            with open(f"llama-{latest_release_tag}-bin-win-cublas-cu12.2.0-x64.zip", "wb") as f:
-                f.write(response.content)
-            with zipfile.ZipFile(f"llama-{latest_release_tag}-bin-win-cublas-cu12.2.0-x64.zip", "r") as zip_ref:
-                zip_ref.extractall(os.path.join(base_dir, "bin"))
-            print("Downloading latest 'llama.cpp' prebuilt Windows binaries...")
-            print("Download and extraction completed successfully.")
-            return latest_release_tag  # Return the latest release tag
-        else:
-            print("Failed to download the release file.")
-    else:
-        print("Failed to fetch the latest release information.")
-
-# Function to download and extract cudart if necessary
-def download_cudart_if_necessary(latest_release_tag):
-    base_dir = os.path.dirname(os.path.abspath(__file__))
-    cudart_dl_dir = os.path.join(base_dir, "bin", "dl")
-    if not os.path.exists(cudart_dl_dir):
-        os.makedirs(cudart_dl_dir)
-
-    cudart_zip_file = os.path.join(cudart_dl_dir, "cudart-llama-bin-win-cu12.2.0-x64.zip")
-    cudart_extracted_files = ["cublas64_12.dll", "cublasLt64_12.dll", "cudart64_12.dll"]
-
-    # Check if all required files exist
-    if all(os.path.exists(os.path.join(base_dir, "bin", file)) for file in cudart_extracted_files):
-        print("Cuda resources already exist. Skipping download.")
-    else:
-        cudart_download_url = f"https://github.com/ggerganov/llama.cpp/releases/download/{latest_release_tag}/cudart-llama-bin-win-cu12.2.0-x64.zip"
-        response = requests.get(cudart_download_url)
-        if response.status_code == 200:
-            with open(cudart_zip_file, "wb") as f:
-                f.write(response.content)
-            with zipfile.ZipFile(cudart_zip_file, "r") as zip_ref:
-                zip_ref.extractall(os.path.join(base_dir, "bin"))
-            print("Preparing 'cuda' resources...")
-            print("Download and extraction of cudart completed successfully.")
-        else:
-            print("Failed to download the cudart release file.")
-
-# Function to collect user input and download the specified model repository
-def download_model_repo():
-    base_dir = os.path.dirname(os.path.abspath(__file__))
-    models_dir = os.path.join(base_dir, "models")
-    if not os.path.exists(models_dir):
-        os.makedirs(models_dir)
-
-    model_id = input("Enter the model ID to download (e.g., huggingface/transformers): ")
-    model_name = model_id.split("/")[-1]
-    model_dir = os.path.join(models_dir, model_name)
-
-    # Download the model repository if it doesn't exist
-    if not os.path.exists(model_dir):
-        revision = input("Enter the revision (branch, tag, or commit) to download (default: main): ") or "main"
-
-        print("Downloading model repository...")
-        snapshot_download(repo_id=model_id, local_dir=model_dir, revision=revision)
-        print("Model repository downloaded successfully.")
-    else:
-        print("Model already exists.")
-
-    # Convert the downloaded model to GGUF F16 format and generate imatrix.dat
-    convert_model_to_gguf_f16(base_dir, model_dir, model_name)
-
-# Function to convert the downloaded model to GGUF F16 format
-def convert_model_to_gguf_f16(base_dir, model_dir, model_name):
-    convert_script = os.path.join(base_dir, "llama.cpp", "convert.py")
-    gguf_dir = os.path.join(base_dir, "models", f"{model_name}-GGUF")
-    gguf_model_path = os.path.join(gguf_dir, f"{model_name}-F16.gguf")
-
-    if not os.path.exists(gguf_dir):
-        os.makedirs(gguf_dir)
-
-    # Execute the conversion command if F16 file doesn't exist
-    if not os.path.exists(gguf_model_path):
-        subprocess.run(["python", convert_script, model_dir, "--outfile", gguf_model_path, "--outtype", "f16"])
-
-        # Delete the original model directory
-        shutil.rmtree(model_dir)
-        print(f"Original model directory '{model_dir}' deleted.")
-
-        # Execute the imatrix command if imatrix.dat doesn't exist
-        imatrix_exe = os.path.join(base_dir, "bin", "imatrix.exe")
-        imatrix_output = os.path.join(gguf_dir, "imatrix.dat")
-        imatrix_txt = os.path.join(base_dir, "imatrix", "imatrix.txt")
-        if not os.path.exists(imatrix_output):
-            subprocess.run([imatrix_exe, "-m", gguf_model_path, "-f", imatrix_txt, "-ngl", "13"])
-            # Move the imatrix.dat file to the GGUF folder
-            shutil.move("imatrix.dat", gguf_dir)
-            print("imatrix.dat generated successfully.")
-
-    # Quantize the models
-    quantize_models(base_dir, model_name)
-
-# Function to quantize models with different options
-def quantize_models(base_dir, model_name):
-    gguf_dir = os.path.join(base_dir, "models", f"{model_name}-GGUF")
-    f16_gguf_path = os.path.join(gguf_dir, f"{model_name}-F16.gguf")
-
-    quantization_options = [
-        "Q4_K_M", "Q4_K_S", "IQ4_NL", "IQ4_XS", "Q5_K_M", 
-        "Q5_K_S", "Q6_K", "Q8_0", "IQ3_M", "IQ3_S", "IQ3_XS", "IQ3_XXS"
-    ]
-
-    for quant_option in quantization_options:
-        quantized_gguf_name = f"{model_name}-{quant_option}-imat.gguf"
-        quantized_gguf_path = os.path.join(gguf_dir, quantized_gguf_name)
-        quantize_command = os.path.join(base_dir, "bin", "quantize.exe")
-        imatrix_path = os.path.join(gguf_dir, "imatrix.dat")
-
-        subprocess.run([quantize_command, "--imatrix", imatrix_path, 
-                        f16_gguf_path, quantized_gguf_path, quant_option], cwd=gguf_dir)
-        print(f"Model quantized with {quant_option} option.")
-
-# Main function to execute the steps
-def main():
-    clone_or_update_llama_cpp()
-    latest_release_tag = download_llama_release()
-    download_cudart_if_necessary(latest_release_tag)
-    download_model_repo()
-    print("Finished.")
-
-if __name__ == "__main__":
-    main()