Imatrix

Dockerfile.bak

@@ -1,63 +0,0 @@
-FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu22.04
-
-ENV DEBIAN_FRONTEND=noninteractive
-RUN apt-get update && \
-    apt-get upgrade -y && \
-    apt-get install -y --no-install-recommends \
-    git \
-    git-lfs \
-    wget \
-    curl \
-    # python build dependencies \
-    build-essential \
-    libssl-dev \
-    zlib1g-dev \
-    libbz2-dev \
-    libreadline-dev \
-    libsqlite3-dev \
-    libncursesw5-dev \
-    xz-utils \
-    tk-dev \
-    libxml2-dev \
-    libxmlsec1-dev \
-    libffi-dev \
-    liblzma-dev \
-    ffmpeg \
-    nvidia-driver-515
-
-RUN useradd -m -u 1000 user
-USER user
-ENV HOME=/home/user \
-    PATH=/home/user/.local/bin:${PATH}
-WORKDIR ${HOME}/app
-
-RUN curl https://pyenv.run | bash
-ENV PATH=${HOME}/.pyenv/shims:${HOME}/.pyenv/bin:${PATH}
-ARG PYTHON_VERSION=3.10.13
-RUN pyenv install ${PYTHON_VERSION} && \
-    pyenv global ${PYTHON_VERSION} && \
-    pyenv rehash && \
-    pip install --no-cache-dir -U pip setuptools wheel && \
-    pip install "huggingface-hub" "hf-transfer" "gradio[oauth]>=4.28.0" "gradio_huggingfacehub_search==0.0.7" "APScheduler"
-
-COPY --chown=1000 . ${HOME}/app
-RUN git clone https://github.com/ggerganov/llama.cpp
-RUN pip install -r llama.cpp/requirements.txt
-
-COPY imatrix_calibration.txt ${HOME}/app/llama.cpp/
-
-ENV PYTHONPATH=${HOME}/app \
-    PYTHONUNBUFFERED=1 \
-    HF_HUB_ENABLE_HF_TRANSFER=1 \
-    GRADIO_ALLOW_FLAGGING=never \
-    GRADIO_NUM_PORTS=1 \
-    GRADIO_SERVER_NAME=0.0.0.0 \
-    GRADIO_THEME=huggingface \
-    TQDM_POSITION=-1 \
-    TQDM_MININTERVAL=1 \
-    SYSTEM=spaces \
-    LD_LIBRARY_PATH=/usr/local/cuda/lib64:${LD_LIBRARY_PATH} \
-    PATH=/usr/local/nvidia/bin:${PATH}
-
-
-ENTRYPOINT ["/bin/bash", "-c", "cd llama.cpp && LLAMA_CUDA=1 make -j && cd .. && /bin/sh start.sh"]

app.py.bak

@@ -1,375 +0,0 @@
-import os
-import shutil
-import subprocess
-os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
-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 gradio_huggingfacehub_search import HuggingfaceHubSearch
-
-from apscheduler.schedulers.background import BackgroundScheduler
-
-from textwrap import dedent
-
-HF_TOKEN = os.environ.get("HF_TOKEN")
-
-def generate_importance_matrix(model_path, train_data_path):
-    imatrix_command = f"./imatrix -m ../{model_path} -f {train_data_path} -ngl 99"  
-
-    os.chdir("llama.cpp")
-
-    compile_command = "LLAMA_CUDA=1 make -j"
-    compile_result = subprocess.run(compile_command, shell=True, capture_output=True, text=True)
-    if compile_result.returncode != 0:
-        raise Exception(f"Error compiling imatrix: {compile_result.stderr}")
-
-
-    print(f"Current working directory: {os.getcwd()}")
-    print(f"Files in the current directory: {os.listdir('.')}")
-
-    if not os.path.isfile(f"../{model_path}"):
-        raise Exception(f"Model file not found: {model_path}")
-
-    print("Running imatrix command...")
-    result = subprocess.run(imatrix_command, shell=True, capture_output=True, text=True)
-
-    os.chdir("..")
-
-    if result.returncode != 0:
-        raise Exception(f"Error generating importance matrix: {result.stderr}")
-    print("Importance matrix generated successfully!")
-
-def split_upload_model(model_path, repo_id, oauth_token: gr.OAuthToken | None, split_max_tensors=256, split_max_size=None):
-    if oauth_token.token is None:
-        raise ValueError("You have to be logged in.")
-    
-    split_cmd = f"llama.cpp/gguf-split --split --split-max-tensors {split_max_tensors}"
-    if split_max_size:
-        split_cmd += f" --split-max-size {split_max_size}"
-    split_cmd += f" {model_path} {model_path.split('.')[0]}"
-    
-    print(f"Split command: {split_cmd}") 
-    
-    result = subprocess.run(split_cmd, shell=True, capture_output=True, text=True)
-    print(f"Split command stdout: {result.stdout}") 
-    print(f"Split command stderr: {result.stderr}") 
-    
-    if result.returncode != 0:
-        raise Exception(f"Error splitting the model: {result.stderr}")
-    print("Model split successfully!")
-     
-    
-    sharded_model_files = [f for f in os.listdir('.') if f.startswith(model_path.split('.')[0])]
-    if sharded_model_files:
-        print(f"Sharded model files: {sharded_model_files}")
-        api = HfApi(token=oauth_token.token)
-        for file in sharded_model_files:
-            file_path = os.path.join('.', file)
-            print(f"Uploading file: {file_path}")
-            try:
-                api.upload_file(
-                    path_or_fileobj=file_path,
-                    path_in_repo=file,
-                    repo_id=repo_id,
-                )
-            except Exception as e:
-                raise Exception(f"Error uploading file {file_path}: {e}")
-    else:
-        raise Exception("No sharded files found.")
-    
-    print("Sharded model has been uploaded successfully!")
-
-def process_model(model_id, q_method, use_imatrix, imatrix_q_method, private_repo, train_data_file, split_model, split_max_tensors, split_max_size, oauth_token: gr.OAuthToken | None):
-    if oauth_token.token is None:
-        raise ValueError("You must be logged in to use GGUF-my-repo")
-    model_name = model_id.split('/')[-1]
-    fp16 = f"{model_name}.fp16.gguf"
-
-    try:
-        api = HfApi(token=oauth_token.token)
-
-        dl_pattern = ["*.md", "*.json", "*.model"]
-
-        pattern = (
-            "*.safetensors"
-            if any(
-                file.path.endswith(".safetensors")
-                for file in api.list_repo_tree(
-                    repo_id=model_id,
-                    recursive=True,
-                )
-            )
-            else "*.bin"
-        )
-
-        dl_pattern += pattern
-
-        api.snapshot_download(repo_id=model_id, local_dir=model_name, local_dir_use_symlinks=False, allow_patterns=dl_pattern)
-        print("Model downloaded successfully!")
-        print(f"Current working directory: {os.getcwd()}")
-        print(f"Model directory contents: {os.listdir(model_name)}")
-
-        conversion_script = "convert-hf-to-gguf.py"
-        fp16_conversion = f"python llama.cpp/{conversion_script} {model_name} --outtype f16 --outfile {fp16}"
-        result = subprocess.run(fp16_conversion, shell=True, capture_output=True)
-        print(result)
-        if result.returncode != 0:
-            raise Exception(f"Error converting to fp16: {result.stderr}")
-        print("Model converted to fp16 successfully!")
-        print(f"Converted model path: {fp16}")
-
-        imatrix_path = "llama.cpp/imatrix.dat"
-
-        if use_imatrix:
-            if train_data_file:
-                train_data_path = train_data_file.name
-            else:
-                train_data_path = "imatrix_calibration.txt"
-
-            print(f"Training data file path: {train_data_path}")
-
-            if not os.path.isfile(train_data_path):
-                raise Exception(f"Training data file not found: {train_data_path}")
-
-            generate_importance_matrix(fp16, train_data_path)
-        else:
-            print("Not using imatrix quantization.")
-        username = whoami(oauth_token.token)["name"]
-        quantized_gguf_name = f"{model_name.lower()}-{imatrix_q_method.lower()}-imat.gguf" if use_imatrix else f"{model_name.lower()}-{q_method.lower()}.gguf"
-        quantized_gguf_path = quantized_gguf_name
-        if use_imatrix:
-            quantise_ggml = f"./llama.cpp/quantize --imatrix {imatrix_path} {fp16} {quantized_gguf_path} {imatrix_q_method}"
-        else:
-            quantise_ggml = f"./llama.cpp/quantize {fp16} {quantized_gguf_path} {q_method}"
-        result = subprocess.run(quantise_ggml, shell=True, capture_output=True)
-        if result.returncode != 0:
-            raise Exception(f"Error quantizing: {result.stderr}")
-        print(f"Quantized successfully with {imatrix_q_method if use_imatrix else q_method} option!")
-        print(f"Quantized model path: {quantized_gguf_path}")
-
-        # Create empty repo
-        new_repo_url = api.create_repo(repo_id=f"{username}/{model_name}-{imatrix_q_method if use_imatrix else q_method}-GGUF", exist_ok=True, private=private_repo)
-        new_repo_id = new_repo_url.repo_id
-        print("Repo created successfully!", new_repo_url)
-
-        try:
-            card = ModelCard.load(model_id, token=oauth_token.token)
-        except:
-            card = ModelCard("")
-        if card.data.tags is None:
-            card.data.tags = []
-        card.data.tags.append("llama-cpp")
-        card.data.tags.append("gguf-my-repo")
-        card.data.base_model = model_id
-        card.text = dedent(
-            f"""
-            # {new_repo_id}
-            This model was converted to GGUF format from [`{model_id}`](https://huggingface.co/{model_id}) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
-            Refer to the [original model card](https://huggingface.co/{model_id}) for more details on the model.
-            
-            ## Use with llama.cpp
-            Install llama.cpp through brew (works on Mac and Linux)
-            
-            ```bash
-            brew install llama.cpp
-            
-            ```
-            Invoke the llama.cpp server or the CLI.
-            
-            ### CLI:
-            ```bash
-            llama --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -p "The meaning to life and the universe is"
-            ```
-            
-            ### Server:
-            ```bash
-            llama-server --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -c 2048
-            ```
-            
-            Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
-
-            Step 1: Clone llama.cpp from GitHub.
-            ```
-            git clone https://github.com/ggerganov/llama.cpp
-            ```
-
-            Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
-            ```
-            cd llama.cpp && LLAMA_CURL=1 make
-            ```
-
-            Step 3: Run inference through the main binary.
-            ```
-            ./main --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -p "The meaning to life and the universe is"
-            ```
-            or 
-            ```
-            ./server --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -c 2048
-            ```
-            """
-        )
-        card.save(f"README.md")
-
-        if split_model:
-            split_upload_model(quantized_gguf_path, new_repo_id, oauth_token, split_max_tensors, split_max_size)
-        else:
-            try:
-                print(f"Uploading quantized model: {quantized_gguf_path}")
-                api.upload_file(
-                    path_or_fileobj=quantized_gguf_path,
-                    path_in_repo=quantized_gguf_name,
-                    repo_id=new_repo_id,
-                )
-            except Exception as e:
-                raise Exception(f"Error uploading quantized model: {e}")
-        
-        
-        imatrix_path = "llama.cpp/imatrix.dat"
-        if os.path.isfile(imatrix_path):
-            try:
-                print(f"Uploading imatrix.dat: {imatrix_path}")
-                api.upload_file(
-                    path_or_fileobj=imatrix_path,
-                    path_in_repo="imatrix.dat",
-                    repo_id=new_repo_id,
-                )
-            except Exception as e:
-                raise Exception(f"Error uploading imatrix.dat: {e}")
-
-        api.upload_file(
-            path_or_fileobj=f"README.md",
-            path_in_repo=f"README.md",
-            repo_id=new_repo_id,
-        )
-        print(f"Uploaded successfully with {imatrix_q_method if use_imatrix else q_method} option!")
-
-        return (
-            f'Find your repo <a href=\'{new_repo_url}\' target="_blank" style="text-decoration:underline">here</a>',
-            "llama.png",
-        )
-    except Exception as e:
-        return (f"Error: {e}", "error.png")
-    finally:
-        shutil.rmtree(model_name, ignore_errors=True)
-        print("Folder cleaned up successfully!")
-
-
-# Create Gradio interface
-with gr.Blocks(css=".gradio-container {max-height: 600px; overflow-y: auto;}") as demo: 
-    gr.Markdown("You must be logged in to use GGUF-my-repo.")
-    gr.LoginButton(min_width=250)
-
-    model_id = HuggingfaceHubSearch(
-        label="Hub Model ID",
-        placeholder="Search for model id on Huggingface",
-        search_type="model",
-    )
-
-    q_method = 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 quantization type",
-        value="Q4_K_M",
-        filterable=False,
-        visible=True
-    )
-
-    imatrix_q_method = gr.Dropdown(
-        ["IQ3_M", "IQ3_XXS", "Q4_K_M", "Q4_K_S", "IQ4_NL", "IQ4_XS", "Q5_K_M", "Q5_K_S"],
-        label="Imatrix Quantization Method",
-        info="GGML imatrix quants type",
-        value="IQ4_NL", 
-        filterable=False,
-        visible=False
-    )
-
-    use_imatrix = gr.Checkbox(
-        value=False,
-        label="Use Imatrix Quantization",
-        info="Use importance matrix for quantization."
-    )
-
-    private_repo = gr.Checkbox(
-        value=False,
-        label="Private Repo",
-        info="Create a private repo under your username."
-    )
-
-    train_data_file = gr.File(
-        label="Training Data File",
-        file_types=["txt"],
-        visible=False
-    )
-
-    split_model = gr.Checkbox(
-        value=False,
-        label="Split Model",
-        info="Shard the model using gguf-split."
-    )
-
-    split_max_tensors = gr.Number(
-        value=256,
-        label="Max Tensors per File",
-        info="Maximum number of tensors per file when splitting model.",
-        visible=False
-    )
-
-    split_max_size = gr.Textbox(
-        label="Max File Size",
-        info="Maximum file size when splitting model (--split-max-size). May leave empty to use the default.",
-        visible=False
-    )
-
-    def update_visibility(use_imatrix):
-        return gr.update(visible=not use_imatrix), gr.update(visible=use_imatrix), gr.update(visible=use_imatrix)
-    
-    use_imatrix.change(
-        fn=update_visibility,
-        inputs=use_imatrix,
-        outputs=[q_method, imatrix_q_method, train_data_file]
-    )
-
-    iface = gr.Interface(
-        fn=process_model,
-        inputs=[
-            model_id,
-            q_method,
-            use_imatrix,
-            imatrix_q_method,
-            private_repo,
-            train_data_file,
-            split_model,
-            split_max_tensors,
-            split_max_size,
-        ],
-        outputs=[
-            gr.Markdown(label="output"),
-            gr.Image(show_label=False),
-        ],
-        title="Create your own GGUF Quants, blazingly fast ⚡!",
-        description="The space takes an HF repo as an input, quantizes it and creates a Public repo containing the selected quant under your HF user namespace.",
-        api_name=False
-    )
-
-    def update_split_visibility(split_model):
-        return gr.update(visible=split_model), gr.update(visible=split_model)
-
-    split_model.change(
-        fn=update_split_visibility,
-        inputs=split_model,
-        outputs=[split_max_tensors, split_max_size]
-    )
-
-def restart_space():
-    HfApi().restart_space(repo_id="ggml-org/gguf-my-repo", token=HF_TOKEN, factory_reboot=True)
-
-scheduler = BackgroundScheduler()
-scheduler.add_job(restart_space, "interval", seconds=21600)
-scheduler.start()
-
-# Launch the interface
-demo.queue(default_concurrency_limit=1, max_size=5).launch(debug=True, show_api=False)

start.sh.bak

@@ -1,5 +0,0 @@
-cd llama.cpp
-make -j quantize gguf-split imatrix
-
-cd ..
-python app.py