Upload 7 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+error.png filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,65 @@
+FROM nvidia/cuda:12.8.0-cudnn-devel-ubuntu24.04
+
+ENV DEBIAN_FRONTEND=noninteractive
+RUN apt-get update && \
+    apt-get upgrade -y && \
+    apt-get install -y --no-install-recommends --fix-missing \
+    git \
+    git-lfs \
+    wget \
+    curl \
+    cmake \
+    # 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-570
+
+# Check if user with UID 1000 exists, if not create it
+RUN id -u 1000 &>/dev/null || useradd -m -u 1000 user
+USER 1000
+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=3.11
+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==0.0.8" "APScheduler"
+
+COPY --chown=1000 . ${HOME}/app
+RUN git clone https://github.com/ggerganov/llama.cpp
+RUN pip install -r llama.cpp/requirements.txt
+
+COPY groups_merged.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 start.sh
+

README.md

@@ -1,12 +1,19 @@
 ---
-title: Gguf
-emoji: 📊
-colorFrom: green
-colorTo: green
-sdk: gradio
-sdk_version: 5.21.0
-app_file: app.py
+title: GGUF My Repo
+emoji: 🦙
+colorFrom: gray
+colorTo: pink
+sdk: docker
+hf_oauth: true
+hf_oauth_scopes:
+  - read-repos
+  - write-repos
+  - manage-repos
 pinned: false
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+To run this space locally:
+1. Login huggingface CLI: `huggingface-cli login`
+2. Run command: `HF_TOKEN=$(cat ~/.cache/huggingface/token) docker compose up`

app.py

@@ -0,0 +1,441 @@
+import os
+import subprocess
+import signal
+os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
+import gradio as gr
+import tempfile
+
+from huggingface_hub import HfApi, ModelCard, whoami
+from gradio_huggingfacehub_search import HuggingfaceHubSearch
+from pathlib import Path
+from textwrap import dedent
+from apscheduler.schedulers.background import BackgroundScheduler
+
+
+# used for restarting the space
+HF_TOKEN = os.environ.get("HF_TOKEN")
+CONVERSION_SCRIPT = "./llama.cpp/convert_hf_to_gguf.py"
+
+# escape HTML for logging
+def escape(s: str) -> str:
+    s = s.replace("&", "&") # Must be done first!
+    s = s.replace("<", "&lt;")
+    s = s.replace(">", "&gt;")
+    s = s.replace('"', "&quot;")
+    s = s.replace("\n", "<br/>")
+    return s
+
+def generate_importance_matrix(model_path: str, train_data_path: str, output_path: str):
+    imatrix_command = [
+        "./llama.cpp/llama-imatrix",
+        "-m", model_path,
+        "-f", train_data_path,
+        "-ngl", "99",
+        "--output-frequency", "10",
+        "-o", output_path,
+    ]
+
+    if not os.path.isfile(model_path):
+        raise Exception(f"Model file not found: {model_path}")
+
+    print("Running imatrix command...")
+    process = subprocess.Popen(imatrix_command, shell=False)
+
+    try:
+        process.wait(timeout=60)  # added wait
+    except subprocess.TimeoutExpired:
+        print("Imatrix computation timed out. Sending SIGINT to allow graceful termination...")
+        process.send_signal(signal.SIGINT)
+        try:
+            process.wait(timeout=5)  # grace period
+        except subprocess.TimeoutExpired:
+            print("Imatrix proc still didn't term. Forecfully terming process...")
+            process.kill()
+
+    print("Importance matrix generation completed.")
+
+def split_upload_model(model_path: str, outdir: str, repo_id: str, oauth_token: gr.OAuthToken | None, split_max_tensors=256, split_max_size=None):
+    print(f"Model path: {model_path}")
+    print(f"Output dir: {outdir}")
+
+    if oauth_token is None or oauth_token.token is None:
+        raise ValueError("You have to be logged in.")
+    
+    split_cmd = [
+        "./llama.cpp/llama-gguf-split",
+        "--split",
+    ]
+    if split_max_size:
+        split_cmd.append("--split-max-size")
+        split_cmd.append(split_max_size)
+    else:
+        split_cmd.append("--split-max-tensors")
+        split_cmd.append(str(split_max_tensors))
+
+    # args for output
+    model_path_prefix = '.'.join(model_path.split('.')[:-1]) # remove the file extension
+    split_cmd.append(model_path)
+    split_cmd.append(model_path_prefix)
+
+    print(f"Split command: {split_cmd}") 
+    
+    result = subprocess.run(split_cmd, shell=False, capture_output=True, text=True)
+    print(f"Split command stdout: {result.stdout}") 
+    print(f"Split command stderr: {result.stderr}") 
+    
+    if result.returncode != 0:
+        stderr_str = result.stderr.decode("utf-8")
+        raise Exception(f"Error splitting the model: {stderr_str}")
+    print("Model split successfully!")
+
+    # remove the original model file if needed
+    if os.path.exists(model_path):
+        os.remove(model_path)
+
+    model_file_prefix = model_path_prefix.split('/')[-1]
+    print(f"Model file name prefix: {model_file_prefix}") 
+    sharded_model_files = [f for f in os.listdir(outdir) if f.startswith(model_file_prefix) and f.endswith(".gguf")]
+    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(outdir, 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 is None or oauth_token.token is None:
+        raise gr.Error("You must be logged in to use GGUF-my-repo")
+
+    # validate the oauth token
+    try:
+        whoami(oauth_token.token)
+    except Exception as e:
+        raise gr.Error("You must be logged in to use GGUF-my-repo")
+
+    model_name = model_id.split('/')[-1]
+
+    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]
+
+        if not os.path.exists("downloads"):
+            os.makedirs("downloads")
+
+        if not os.path.exists("outputs"):
+            os.makedirs("outputs")
+
+        with tempfile.TemporaryDirectory(dir="outputs") as outdir:
+            fp16 = str(Path(outdir)/f"{model_name}.fp16.gguf")
+
+            with tempfile.TemporaryDirectory(dir="downloads") as tmpdir:
+                # Keep the model name as the dirname so the model name metadata is populated correctly
+                local_dir = Path(tmpdir)/model_name
+                print(local_dir)
+                api.snapshot_download(repo_id=model_id, local_dir=local_dir, 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(local_dir)}")
+
+                config_dir = local_dir/"config.json"
+                adapter_config_dir = local_dir/"adapter_config.json"
+                if os.path.exists(adapter_config_dir) and not os.path.exists(config_dir):
+                    raise Exception('adapter_config.json is present.<br/><br/>If you are converting a LoRA adapter to GGUF, please use <a href="https://huggingface.co/spaces/ggml-org/gguf-my-lora" target="_blank" style="text-decoration:underline">GGUF-my-lora</a>.')
+
+                result = subprocess.run([
+                    "python", CONVERSION_SCRIPT, local_dir, "--outtype", "f16", "--outfile", fp16
+                ], shell=False, capture_output=True)
+                print(result)
+                if result.returncode != 0:
+                    stderr_str = result.stderr.decode("utf-8")
+                    raise Exception(f"Error converting to fp16: {stderr_str}")
+                print("Model converted to fp16 successfully!")
+                print(f"Converted model path: {fp16}")
+
+            imatrix_path = Path(outdir)/"imatrix.dat"
+
+            if use_imatrix:
+                if train_data_file:
+                    train_data_path = train_data_file.name
+                else:
+                    train_data_path = "llama.cpp/groups_merged.txt" #fallback calibration dataset
+
+                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, imatrix_path)
+            else:
+                print("Not using imatrix quantization.")
+            
+            # Quantize the model
+            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 = str(Path(outdir)/quantized_gguf_name)
+            if use_imatrix:
+                quantise_ggml = [
+                    "./llama.cpp/llama-quantize",
+                    "--imatrix", imatrix_path, fp16, quantized_gguf_path, imatrix_q_method
+                ]
+            else:
+                quantise_ggml = [
+                    "./llama.cpp/llama-quantize",
+                    fp16, quantized_gguf_path, q_method
+                ]
+            result = subprocess.run(quantise_ggml, shell=False, capture_output=True)
+            if result.returncode != 0:
+                stderr_str = result.stderr.decode("utf-8")
+                raise Exception(f"Error quantizing: {stderr_str}")
+            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
+            username = whoami(oauth_token.token)["name"]
+            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-cli --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.
+                ```
+                ./llama-cli --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -p "The meaning to life and the universe is"
+                ```
+                or 
+                ```
+                ./llama-server --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -c 2048
+                ```
+                """
+            )
+            readme_path = Path(outdir)/"README.md"
+            card.save(readme_path)
+
+            if split_model:
+                split_upload_model(str(quantized_gguf_path), outdir, 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}")
+            
+            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=readme_path,
+                path_in_repo="README.md",
+                repo_id=new_repo_id,
+            )
+            print(f"Uploaded successfully with {imatrix_q_method if use_imatrix else q_method} option!")
+
+        # end of the TemporaryDirectory(dir="outputs") block; temporary outputs are deleted here
+
+        return (
+            f'<h1>✅ DONE</h1><br/>Find your repo here: <a href="{new_repo_url}" target="_blank" style="text-decoration:underline">{new_repo_id}</a>',
+            "llama.png",
+        )
+    except Exception as e:
+        return (f'<h1>❌ ERROR</h1><br/><pre style="white-space:pre-wrap;">{escape(str(e))}</pre>', "error.png")
+
+
+css="""/* Custom CSS to allow scrolling */
+.gradio-container {overflow-y: auto;}
+"""
+# Create Gradio interface
+with gr.Blocks(css=css) 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. Accepted suffixes: M, G. Example: 256M, 5G",
+        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)

docker-compose.yml

@@ -0,0 +1,16 @@
+# Docker compose file to LOCAL development
+
+services:
+  gguf-my-repo:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    image: gguf-my-repo
+    container_name: gguf-my-repo
+    ports:
+      - "7860:7860"
+    volumes:
+      - .:/home/user/app
+    environment:
+      - RUN_LOCALLY=1
+      - HF_TOKEN=${HF_TOKEN}

start.sh

@@ -0,0 +1,21 @@
+#!/bin/bash
+
+if [ ! -d "llama.cpp" ]; then
+  # only run in dev env
+  git clone https://github.com/ggerganov/llama.cpp
+fi
+
+export GGML_CUDA=OFF
+if [[ -z "${RUN_LOCALLY}" ]]; then
+  # enable CUDA if NOT running locally
+  export GGML_CUDA=ON
+fi
+
+cd llama.cpp
+cmake -B build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=${GGML_CUDA}
+cmake --build build --config Release -j --target llama-quantize llama-gguf-split llama-imatrix
+cp ./build/bin/llama-* .
+rm -rf build
+
+cd ..
+python app.py