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("<", "<")
s = s.replace(">", ">")
s = s.replace('"', """)
s = s.replace("\n", "
")
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 all-gguf-same-where")
# validate the oauth token
try:
whoami(oauth_token.token)
except Exception as e:
raise gr.Error("You must be logged in to use all-gguf-same-where")
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.
If you are converting a LoRA adapter to GGUF, please use GGUF-my-lora.')
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/build/bin/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}-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("matrixportal")
card.data.base_model = model_id
def get_license_info(card):
"""Model kartından lisans bilgisini güvenle çeker"""
if not hasattr(card, 'data'):
return "Unknown"
# Lisans bilgisini alma (license veya model_license alanlarından)
license_info = getattr(card.data, 'license', getattr(card.data, 'model_license', 'Unknown'))
# Dictionary formatı kontrolü
if isinstance(license_info, dict):
license_info = license_info.get('name', 'Unknown')
return str(license_info).strip() or "Unknown"
def get_license_info(card):
"""Model kartından lisans bilgisini güvenle çeker"""
if not hasattr(card, 'data'):
return "Unknown"
# Lisans bilgisini alma (license veya model_license alanlarından)
license_info = getattr(card.data, 'license', getattr(card.data, 'model_license', 'Unknown'))
# Dictionary formatı kontrolü
if isinstance(license_info, dict):
license_info = license_info.get('name', 'Unknown')
return str(license_info).strip() or "Unknown"
def format_license_text(license_name):
"""Lisans adını linkli hale getirir"""
license_name = str(license_name).lower().replace(' ', '-')
license_links = {
'apache-2.0': '[Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)',
'mit': '[MIT](https://opensource.org/licenses/MIT)',
'llama3.2': '[Llama 3 Community License](https://llama.meta.com/llama3/license)',
'cc-by-nc-4.0': '[CC-BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/)',
'openrail': '[OpenRAIL](https://huggingface.co/spaces/CompVis/stable-diffusion-license)'
}
return license_links.get(license_name, license_name)
# Lisans bilgisini al ve formatla
license_info = get_license_info(card)
formatted_license = format_license_text(license_info)
card.text = dedent(
f"""
- **Base model:** [{model_id}](https://huggingface.co/{model_id})
- **License:** {formatted_license}
Quantized with llama.cpp using [all-gguf-same-where](https://huggingface.co/spaces/matrixportal/all-gguf-same-where)
"""
)
readme_path = Path(outdir)/"README.md"
card.save(readme_path)
# Quant listesi oluşturma
quant_list = f"""
## ✅ Quantized Models Download List
### 🔍 Recommended Quantizations
- **✨ General CPU Use:** [`Q4_K_M`](https://huggingface.co/{new_repo_id}/resolve/main/{model_name.lower()}-q4_k_m.gguf) (Best balance of speed/quality)
- **📱 ARM Devices:** [`Q4_0`](https://huggingface.co/{new_repo_id}/resolve/main/{model_name.lower()}-q4_0.gguf) (Optimized for ARM CPUs)
- **🏆 Maximum Quality:** [`Q8_0`](https://huggingface.co/{new_repo_id}/resolve/main/{model_name.lower()}-q8_0.gguf) (Near-original quality)
### 📦 Full Quantization Options
| 🚀 Download | 🔢 Type | 📝 Notes |
|:---------|:-----|:------|
| [Download](https://huggingface.co/{new_repo_id}/resolve/main/{model_name.lower()}-q2_k.gguf) |  | Basic quantization |
| [Download](https://huggingface.co/{new_repo_id}/resolve/main/{model_name.lower()}-q3_k_s.gguf) |  | Small size |
| [Download](https://huggingface.co/{new_repo_id}/resolve/main/{model_name.lower()}-q3_k_m.gguf) |  | Balanced quality |
| [Download](https://huggingface.co/{new_repo_id}/resolve/main/{model_name.lower()}-q3_k_l.gguf) |  | Better quality |
| [Download](https://huggingface.co/{new_repo_id}/resolve/main/{model_name.lower()}-q4_0.gguf) |  | Fast on ARM |
| [Download](https://huggingface.co/{new_repo_id}/resolve/main/{model_name.lower()}-q4_k_s.gguf) |  | Fast, recommended |
| [Download](https://huggingface.co/{new_repo_id}/resolve/main/{model_name.lower()}-q4_k_m.gguf) |  ⭐ | Best balance |
| [Download](https://huggingface.co/{new_repo_id}/resolve/main/{model_name.lower()}-q5_0.gguf) |  | Good quality |
| [Download](https://huggingface.co/{new_repo_id}/resolve/main/{model_name.lower()}-q5_k_s.gguf) |  | Balanced |
| [Download](https://huggingface.co/{new_repo_id}/resolve/main/{model_name.lower()}-q5_k_m.gguf) |  | High quality |
| [Download](https://huggingface.co/{new_repo_id}/resolve/main/{model_name.lower()}-q6_k.gguf) |  🏆 | Very good quality |
| [Download](https://huggingface.co/{new_repo_id}/resolve/main/{model_name.lower()}-q8_0.gguf) |  ⚡ | Fast, best quality |
| [Download](https://huggingface.co/{new_repo_id}/resolve/main/{model_name.lower()}-f16.gguf) |  | Maximum accuracy |
💡 **Tip:** Use `F16` for maximum precision when quality is critical
---
# 🚀 Applications and Tools for Locally Quantized LLMs
## 🖥️ Desktop Applications
| Application | Description | Download Link |
|-----------------|----------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------|
| **Llama.cpp** | A fast and efficient inference engine for GGUF models. | [GitHub Repository](https://github.com/ggml-org/llama.cpp) |
| **Ollama** | A streamlined solution for running LLMs locally. | [Website](https://ollama.com/) |
| **AnythingLLM** | An AI-powered knowledge management tool. | [GitHub Repository](https://github.com/Mintplex-Labs/anything-llm) |
| **Open WebUI** | A user-friendly web interface for running local LLMs. | [GitHub Repository](https://github.com/open-webui/open-webui) |
| **GPT4All** | A user-friendly desktop application supporting various LLMs, compatible with GGUF models. | [GitHub Repository](https://github.com/nomic-ai/gpt4all) |
| **LM Studio** | A desktop application designed to run and manage local LLMs, supporting GGUF format. | [Website](https://lmstudio.ai/) |
| **GPT4All Chat**| A chat application compatible with GGUF models for local, offline interactions. | [GitHub Repository](https://github.com/nomic-ai/gpt4all) |
---
## 📱 Mobile Applications
| Application | Description | Download Link |
|-------------------|----------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------|
| **ChatterUI** | A simple and lightweight LLM app for mobile devices. | [GitHub Repository](https://github.com/Vali-98/ChatterUI) |
| **Maid** | Mobile Artificial Intelligence Distribution for running AI models on mobile devices. | [GitHub Repository](https://github.com/Mobile-Artificial-Intelligence/maid) |
| **PocketPal AI** | A mobile AI assistant powered by local models. | [GitHub Repository](https://github.com/a-ghorbani/pocketpal-ai) |
| **Layla** | A flexible platform for running various AI models on mobile devices. | [Website](https://www.layla-network.ai/) |
---
## 🎨 Image Generation Applications
| Application | Description | Download Link |
|-------------------------------------|----------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------|
| **Stable Diffusion** | An open-source AI model for generating images from text. | [GitHub Repository](https://github.com/CompVis/stable-diffusion) |
| **Stable Diffusion WebUI** | A web application providing access to Stable Diffusion models via a browser interface. | [GitHub Repository](https://github.com/AUTOMATIC1111/stable-diffusion-webui) |
| **Local Dream** | Android Stable Diffusion with Snapdragon NPU acceleration. Also supports CPU inference. | [GitHub Repository](https://github.com/xororz/local-dream) |
| **Stable-Diffusion-Android (SDAI)** | An open-source AI art application for Android devices, enabling digital art creation. | [GitHub Repository](https://github.com/ShiftHackZ/Stable-Diffusion-Android) |
---
"""
# README'yi güncelle (ModelCard kullanarak)
card.text += quant_list
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'✅ DONE
Find your repo here: {new_repo_id}',
"llama.png",
)
except Exception as e:
return (f'❌ ERROR
{escape(str(e))}', "error.png")
css="""/* Custom CSS to allow scrolling */
.gradio-container {overflow-y: auto;}
"""
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", "f16"],
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
)
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
)
# Create Gradio interface
with gr.Blocks(css=css) as demo:
gr.Markdown("You must be logged in to use all-gguf-same-where.")
gr.LoginButton(min_width=250)
iface.render()
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 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]
)
def restart_space():
HfApi().restart_space(repo_id="matrixportal/all-gguf-same-where", 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)