gguf-my-repo / app.py
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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 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"./llama-imatrix -m ../{model_path} -f {train_data_path} -ngl 99 --output-frequency 10"
os.chdir("llama.cpp")
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...")
process = subprocess.Popen(imatrix_command, shell=True)
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()
os.chdir("..")
print("Importance matrix generation completed.")
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 = "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)
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/llama-quantize --imatrix {imatrix_path} {fp16} {quantized_gguf_path} {imatrix_q_method}"
else:
quantise_ggml = f"./llama.cpp/llama-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-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
```
"""
)
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!")
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.",
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)