Instructions to use mohammadjafari-dev/local-ai-toolkit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
NeMo
How to use mohammadjafari-dev/local-ai-toolkit with NeMo:
```
# tag did not correspond to a valid NeMo domain.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use mohammadjafari-dev/local-ai-toolkit with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mohammadjafari-dev/local-ai-toolkit:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf mohammadjafari-dev/local-ai-toolkit:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mohammadjafari-dev/local-ai-toolkit:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf mohammadjafari-dev/local-ai-toolkit:UD-Q4_K_XL

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf mohammadjafari-dev/local-ai-toolkit:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama cli -hf mohammadjafari-dev/local-ai-toolkit:UD-Q4_K_XL

Use Docker

docker model run hf.co/mohammadjafari-dev/local-ai-toolkit:UD-Q4_K_XL

Llama new
LM Studio
Jan

vLLM

How to use mohammadjafari-dev/local-ai-toolkit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mohammadjafari-dev/local-ai-toolkit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mohammadjafari-dev/local-ai-toolkit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mohammadjafari-dev/local-ai-toolkit:UD-Q4_K_XL

Ollama
How to use mohammadjafari-dev/local-ai-toolkit with Ollama:
```
ollama run hf.co/mohammadjafari-dev/local-ai-toolkit:UD-Q4_K_XL
```
Unsloth Desktop

How to use mohammadjafari-dev/local-ai-toolkit with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mohammadjafari-dev/local-ai-toolkit:UD-Q4_K_XL

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mohammadjafari-dev/local-ai-toolkit:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mohammadjafari-dev/local-ai-toolkit with Docker Model Runner:
```
docker model run hf.co/mohammadjafari-dev/local-ai-toolkit:UD-Q4_K_XL
```

Lemonade

How to use mohammadjafari-dev/local-ai-toolkit with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mohammadjafari-dev/local-ai-toolkit:UD-Q4_K_XL

Run and chat with the model

lemonade run user.local-ai-toolkit-UD-Q4_K_XL

List all available models

lemonade list

Hermes Agent

How to use mohammadjafari-dev/local-ai-toolkit with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mohammadjafari-dev/local-ai-toolkit:UD-Q4_K_XL

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default mohammadjafari-dev/local-ai-toolkit:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mohammadjafari-dev/local-ai-toolkit with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mohammadjafari-dev/local-ai-toolkit:UD-Q4_K_XL

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "mohammadjafari-dev/local-ai-toolkit:UD-Q4_K_XL" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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New Section"],"metadata":{"id":"2pGdDKXDxAr-"}},{"cell_type":"code","source":["import os\n","import re\n","from google.colab import drive\n","\n","# ۱. اتصال به گوگل درایو (در صورت عدم اتصال)\n","print(\"🔗 Checking Google Drive connection...\")\n","# drive.mount('/content/drive', force_remount=False)\n","\n","# ۲. تنظیم مسیر اصلی جعبه‌ابزار شما\n","BASE_PATH = \"/content/drive/MyDrive/Local AI Toolkit\"\n","\n","# تابع کمکی برای تبدیل بایت به واحدهای قابل خواندن\n","def get_readable_size(size_in_bytes):\n"," for unit in ['Bytes', 'KB', 'MB', 'GB', 'TB']:\n"," if size_in_bytes < 1024.0:\n"," return f\"{size_in_bytes:.2f} {unit}\"\n"," size_in_bytes /= 1024.0\n"," return f\"{size_in_bytes:.2f} PB\"\n","\n","# تابع کمکی برای محاسبه حجم کامل و واقعی یک پوشه (شامل تمام محتویات و پارت‌ها)\n","def get_dir_size(path):\n"," total_size = 0\n"," if not os.path.exists(path):\n"," return 0\n"," for dirpath, dirnames, filenames in os.walk(path):\n"," for f in filenames:\n"," fp = os.path.join(dirpath, f)\n"," if not os.path.islink(fp):\n"," total_size += os.path.getsize(fp)\n"," return total_size\n","\n","print(\"\\n\" + \"=\"*90)\n","print(\"🔍 [LOCAL AI TOOLKIT SCANNER] - Full Directory & Model Storage Metrics\")\n","print(\"=\"*90)\n","\n","if not os.path.exists(BASE_PATH):\n"," print(f\"❌ Error: The base path '{BASE_PATH}' does not exist.\")\n"," print(\"Please make sure your Google Drive is mounted correctly and the folder exists.\")\n","else:\n"," # یافتن تمام پوشه‌های دسته‌بندی اصلی (سطح اول)\n"," categories = sorted([d for d in os.listdir(BASE_PATH) if os.path.isdir(os.path.join(BASE_PATH, d))])\n","\n"," total_models_count = 0\n"," total_toolkit_size = 0 # حجم کل فیزیکی مدل‌های شناسایی‌شده\n","\n"," for category in categories:\n"," category_path = os.path.join(BASE_PATH, category)\n"," category_folder_size = get_dir_size(category_path)\n","\n"," print(f\"\\n📂 Category: 📦 {category} | 🗄️ Total Folder Size: {get_readable_size(category_folder_size)}\")\n"," print(\"-\" * 80)\n","\n"," category_has_models = False\n"," is_stt_category = \"STT\" in category or \"6-\" in category\n","\n"," # گام اول: بررسی وجود فایل‌های RAR به صورت مستقیم در ریشه خودِ پوشه دسته‌بندی\n"," direct_rar_files = [f for f in os.listdir(category_path) if f.endswith('.rar')]\n","\n"," if direct_rar_files:\n"," unique_direct_models = set()\n"," for rar_file in direct_rar_files:\n"," clean_name = re.sub(r'\\.part\\d+\\.rar$', '', rar_file)\n"," clean_name = re.sub(r'\\.rar$', '', clean_name)\n"," unique_direct_models.add(clean_name)\n","\n"," # محاسبه حجم و چاپ مدل‌های ریشه پوشه\n"," for model_name in sorted(unique_direct_models):\n"," model_size = 0\n"," for rar_file in direct_rar_files:\n"," c_name = re.sub(r'\\.part\\d+\\.rar$', '', rar_file)\n"," c_name = re.sub(r'\\.rar$', '', c_name)\n"," if c_name == model_name:\n"," file_path = os.path.join(category_path, rar_file)\n"," model_size += os.path.getsize(file_path)\n","\n"," total_toolkit_size += model_size\n"," readable_size = get_readable_size(model_size)\n"," display_ext = \" (Asset Pack)\" if is_stt_category else \".gguf\"\n"," print(f\" 🔹 Model (Root): {model_name}{display_ext} | 💾 Model Size: {readable_size}\")\n"," total_models_count += 1\n"," category_has_models = True\n","\n"," # گام دوم: بررسی وجود زیرپوشه‌های تخصصی (سطح دوم و سوم)\n"," subfolders = sorted([s for s in os.listdir(category_path) if os.path.isdir(os.path.join(category_path, s))])\n","\n"," for subfolder in subfolders:\n"," subfolder_path = os.path.join(category_path, subfolder)\n","\n"," # بررسی هوشمند پله سوم: آیا این پوشه خودش شامل زیرپوشه‌های دیگری است؟ (مثل Quality یا Speed)\n"," sub_subfolders = sorted([ss for ss in os.listdir(subfolder_path) if os.path.isdir(os.path.join(subfolder_path, ss))])\n","\n"," if sub_subfolders:\n"," # اسکن ساختار ۳ سطحی متناسب با پوشه مدرن STT\n"," for sub_subfolder in sub_subfolders:\n"," sub_subfolder_path = os.path.join(subfolder_path, sub_subfolder)\n"," rar_files = [f for f in os.listdir(sub_subfolder_path) if f.endswith('.rar')]\n","\n"," if rar_files:\n"," sub_subfolder_size = get_dir_size(sub_subfolder_path)\n"," print(f\" └── 🛠️ Subfolder: {subfolder}/{sub_subfolder} | 🗂️ Folder Size: {get_readable_size(sub_subfolder_size)}\")\n","\n"," unique_models = set()\n"," for rar_file in rar_files:\n"," clean_name = re.sub(r'\\.part\\d+\\.rar$', '', rar_file)\n"," clean_name = re.sub(r'\\.rar$', '', clean_name)\n"," unique_models.add(clean_name)\n","\n"," for model_name in sorted(unique_models):\n"," model_size = 0\n"," for rar_file in rar_files:\n"," c_name = re.sub(r'\\.part\\d+\\.rar$', '', rar_file)\n"," c_name = re.sub(r'\\.rar$', '', c_name)\n"," if c_name == model_name:\n"," file_path = os.path.join(sub_subfolder_path, rar_file)\n"," model_size += os.path.getsize(file_path)\n","\n"," total_toolkit_size += model_size\n"," readable_size = get_readable_size(model_size)\n"," display_ext = \" (Asset Pack)\" if is_stt_category else \".gguf\"\n"," print(f\" 🔹 Model: {model_name}{display_ext} | 💾 Model Size: {readable_size}\")\n"," total_models_count += 1\n"," category_has_models = True\n"," else:\n"," # اسکن استاندارد ساختار ۲ سطحی قدیمی\n"," rar_files = [f for f in os.listdir(subfolder_path) if f.endswith('.rar')]\n","\n"," if rar_files:\n"," subfolder_folder_size = get_dir_size(subfolder_path)\n"," print(f\" └── 🛠️ Subfolder: {subfolder} | 🗂️ Folder Size: {get_readable_size(subfolder_folder_size)}\")\n","\n"," unique_models = set()\n"," for rar_file in rar_files:\n"," clean_name = re.sub(r'\\.part\\d+\\.rar$', '', rar_file)\n"," clean_name = re.sub(r'\\.rar$', '', clean_name)\n"," unique_models.add(clean_name)\n","\n"," for model_name in sorted(unique_models):\n"," model_size = 0\n"," for rar_file in rar_files:\n"," c_name = re.sub(r'\\.part\\d+\\.rar$', '', rar_file)\n"," c_name = re.sub(r'\\.rar$', '', c_name)\n"," if c_name == model_name:\n"," file_path = os.path.join(subfolder_path, rar_file)\n"," model_size += os.path.getsize(file_path)\n","\n"," total_toolkit_size += model_size\n"," readable_size = get_readable_size(model_size)\n"," display_ext = \" (Asset Pack)\" if is_stt_category else \".gguf\"\n"," print(f\" 🔹 Model: {model_name}{display_ext} | 💾 Model Size: {readable_size}\")\n"," total_models_count += 1\n"," category_has_models = True\n","\n"," if not category_has_models:\n"," print(\" (No models found in this category yet)\")\n","\n"," # گزارش نهایی\n"," print(\"\\n\" + \"=\"*90)\n"," print(f\"🏁 SCAN COMPLETED!\")\n"," print(f\"📊 Total Unique Models Identified: {total_models_count}\")\n"," print(f\"🗄️ Total Toolkit Storage Size (RAR Archives Total): {get_readable_size(total_toolkit_size)}\")\n"," print(\"=\"*90)\n"," print(\"\"\"and also i have gemma-4-31B-it-UD-Q4_K_XL.gguf , gemma-4-31B-mmproj-F16.gguf \\n and\\nNAME ID SIZE MODIFIED\n","all-minilm:l6-v2 1b226e2802db 45 MB 4 months ago\n","translategemma:12b c2f9a9ca1ec7 8.1 GB 5 months ago\n","gpt-oss:20b 17052f91a42e 13 GB 5 months ago\n","gpt-oss-20b-local:latest 8c4347bf41d2 12 GB 5 months ago\n","qwen3:14b bdbd181c33f2 9.3 GB 10 months ago\"\"\")"],"metadata":{"id":"ZqizvnWBE9q4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1783896813347,"user_tz":-210,"elapsed":571,"user":{"displayName":"Aditya Kumar sharma","userId":"13769898468619597844"}},"outputId":"e7304eee-83db-4244-d563-3c301521d2bb"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["🔗 Checking Google Drive connection...\n","\n","==========================================================================================\n","🔍 [LOCAL AI TOOLKIT SCANNER] - Full Directory & Model Storage Metrics\n","==========================================================================================\n","\n","📂 Category: 📦 1-Universal_Foundation_LLMs | 🗄️ Total Folder Size: 26.95 GB\n","--------------------------------------------------------------------------------\n"," └── 🛠️ Subfolder: Quality | 🗂️ Folder Size: 20.82 GB\n"," 🔹 Model: Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf | 💾 Model Size: 20.82 GB\n"," └── 🛠️ Subfolder: Speed | 🗂️ Folder Size: 6.13 GB\n"," 🔹 Model: Qwen3.5-9B-Q5_K_M.gguf | 💾 Model Size: 6.13 GB\n","\n","📂 Category: 📦 2-Coding_Agents | 🗄️ Total Folder Size: 64.53 GB\n","--------------------------------------------------------------------------------\n"," └── 🛠️ Subfolder: 1-Architect_and_Executor | 🗂️ Folder Size: 20.23 GB\n"," 🔹 Model: Qwen3-Coder-30B-A3B-Instruct-Q5_K_M.gguf | 💾 Model Size: 20.23 GB\n"," └── 🛠️ Subfolder: 2-Detective_and_Debugger | 🗂️ Folder Size: 18.49 GB\n"," 🔹 Model: DeepSeek-R1-Distill-Qwen-32B-Q4_K_M.gguf | 💾 Model Size: 18.49 GB\n"," └── 🛠️ Subfolder: 3-Inline_Autocomplete | 🗂️ Folder Size: 5.07 GB\n"," 🔹 Model: qwen2.5-coder-7b-instruct-q5_k_m.gguf | 💾 Model Size: 5.07 GB\n"," └── 🛠️ Subfolder: 4-Architect_and_Executor_speed | 🗂️ Folder Size: 12.37 GB\n"," 🔹 Model: Qwen2.5_Coder_14B_Instruct_Q4_K_M.gguf | 💾 Model Size: 12.37 GB\n"," └── 🛠️ Subfolder: 5-Detective_and_Debugger_speed | 🗂️ Folder Size: 8.37 GB\n"," 🔹 Model: DeepSeek_R1_Distill_Qwen_14B_Q4_K_M.gguf | 💾 Model Size: 8.37 GB\n","\n","📂 Category: 📦 3-agent | 🗄️ Total Folder Size: 16.32 GB\n","--------------------------------------------------------------------------------\n"," 🔹 Model (Root): GLM-4.7-Flash-UD-Q4_K_XL.gguf | 💾 Model Size: 16.32 GB\n","\n","📂 Category: 📦 4-Document_Analysis_and_RAG | 🗄️ Total Folder Size: 17.05 GB\n","--------------------------------------------------------------------------------\n"," 🔹 Model (Root): gemma-4-12b-it-UD-Q4_K_XL.gguf | 💾 Model Size: 6.86 GB\n"," 🔹 Model (Root): mmproj-F16.gguf | 💾 Model Size: 167.00 MB\n"," 🔹 Model (Root): mtp-gemma-4-12b-it.gguf | 💾 Model Size: 443.56 MB\n"," └── 🛠️ Subfolder: 1-Embeddings | 🗂️ Folder Size: 2.14 GB\n"," 🔹 Model: BGE_M3_Core_Embedding.gguf | 💾 Model Size: 2.14 GB\n"," └── 🛠️ Subfolder: 2-Rerankers | 🗂️ Folder Size: 2.14 GB\n"," 🔹 Model: BGE_Reranker_V2_M3_Core.gguf | 💾 Model Size: 2.14 GB\n"," └── 🛠️ Subfolder: 3-GraphRAG_Extractors | 🗂️ Folder Size: 3.78 GB\n"," 🔹 Model: NuExtract_1_5_GraphRAG_Extractor.gguf | 💾 Model Size: 3.78 GB\n"," └── 🛠️ Subfolder: 4-Web_Distillers | 🗂️ Folder Size: 1.53 GB\n"," 🔹 Model: Jina_Reader_LM_1_5B_Web_Distiller.gguf | 💾 Model Size: 1.53 GB\n","\n","📂 Category: 📦 5-TTS | 🗄️ Total Folder Size: 19.64 GB\n","--------------------------------------------------------------------------------\n"," └── 🛠️ Subfolder: MULTI_SPEAKER | 🗂️ Folder Size: 7.16 GB\n"," 🔹 Model: Dia2_2B_Dialogue.gguf | 💾 Model Size: 7.16 GB\n"," └── 🛠️ Subfolder: TTS_PERSIAN | 🗂️ Folder Size: 7.02 GB\n"," 🔹 Model: Chatterbox_TTS_Persian.gguf | 💾 Model Size: 2.00 GB\n"," 🔹 Model: F5_TTS_Persian.gguf | 💾 Model Size: 5.02 GB\n"," └── 🛠️ Subfolder: VOICE_DESIGN | 🗂️ Folder Size: 4.21 GB\n"," 🔹 Model: Qwen3_TTS_12Hz_VoiceDesign.gguf | 💾 Model Size: 4.21 GB\n"," └── 🛠️ Subfolder: ZERO_SHOT_CLONING | 🗂️ Folder Size: 1.26 GB\n"," 🔹 Model: F5_TTS_v1_Base.gguf | 💾 Model Size: 1.26 GB\n","\n","📂 Category: 📦 6-STT | 🗄️ Total Folder Size: 22.65 GB\n","--------------------------------------------------------------------------------\n"," └── 🛠️ Subfolder: Audio_Preprocessing_Filters | 🗂️ Folder Size: 291.09 MB\n"," 🔹 Model: Studio_Vocal_Isolation_DemucsV4 (Asset Pack) | 💾 Model Size: 288.87 MB\n"," 🔹 Model: Universal_Silence_Noise_Cutter_Silero (Asset Pack) | 💾 Model Size: 2.22 MB\n"," └── 🛠️ Subfolder: Quality/Multi_Language | 🗂️ Folder Size: 2.88 GB\n"," 🔹 Model: Whisper_Large_V3_Global_FP16 (Asset Pack) | 💾 Model Size: 2.88 GB\n"," └── 🛠️ Subfolder: Quality/Persian_ASR | 🗂️ Folder Size: 6.18 GB\n"," 🔹 Model: Anti_Hallucination_FastConformer_Fa (Asset Pack) | 💾 Model Size: 437.96 MB\n"," 🔹 Model: Whisper_Persian_V4_FP16 (Asset Pack) | 💾 Model Size: 5.75 GB\n"," └── 🛠️ Subfolder: Quality/Speaker_Diarization | 🗂️ Folder Size: 1.57 KB\n"," 🔹 Model: Pyannote_Speaker_Diarization_v3.1 (Asset Pack) | 💾 Model Size: 1.57 KB\n"," └── 🛠️ Subfolder: Quality/Translation | 🗂️ Folder Size: 8.78 GB\n"," 🔹 Model: Seamless_M4T_V2_Large_Translation (Asset Pack) | 💾 Model Size: 8.78 GB\n"," └── 🛠️ Subfolder: Speed/Multi_Language | 🗂️ Folder Size: 1.51 GB\n"," 🔹 Model: Whisper_Turbo_Global_Speed (Asset Pack) | 💾 Model Size: 1.51 GB\n"," └── 🛠️ Subfolder: Speed/Persian_ASR | 🗂️ Folder Size: 3.02 GB\n"," 🔹 Model: Whisper_Turbo_Persian_Speed (Asset Pack) | 💾 Model Size: 3.02 GB\n","\n","📂 Category: 📦 7-Multimodal | 🗄️ Total Folder Size: 24.37 GB\n","--------------------------------------------------------------------------------\n"," └── 🛠️ Subfolder: Quality | 🗂️ Folder Size: 18.52 GB\n"," 🔹 Model: Qwen3_Omni_30B_A3B_Quality.gguf | 💾 Model Size: 18.52 GB\n"," └── 🛠️ Subfolder: Speed | 🗂️ Folder Size: 5.86 GB\n"," 🔹 Model: Gemma4_E4B_Speed_Speculative.gguf | 💾 Model Size: 5.86 GB\n","\n","📂 Category: 📦 8-Agent_Orchestration | 🗄️ Total Folder Size: 57.55 GB\n","--------------------------------------------------------------------------------\n"," └── 🛠️ Subfolder: Agentic_Workers | 🗂️ Folder Size: 6.87 GB\n"," 🔹 Model: Gemma4_12B_Agentic_Worker_v2.gguf | 💾 Model Size: 6.87 GB\n"," └─�� 🛠️ Subfolder: Autonomous_Research | 🗂️ Folder Size: 17.44 GB\n"," 🔹 Model: Agents_A1_35B_MoE_Calibrated_CEO.gguf | 💾 Model Size: 17.44 GB\n"," └── 🛠️ Subfolder: Guardrails | 🗂️ Folder Size: 4.58 GB\n"," 🔹 Model: Llama_Guard_3_8B_i1_Firewall.gguf | 💾 Model Size: 4.58 GB\n"," └── 🛠️ Subfolder: Quality | 🗂️ Folder Size: 20.27 GB\n"," 🔹 Model: Hermes_4_3_36B_Quality_Orchestrator.gguf | 💾 Model Size: 20.27 GB\n"," └── 🛠️ Subfolder: Speed | 🗂️ Folder Size: 8.38 GB\n"," 🔹 Model: Hermes_4_14B_Speed_Guardrail.gguf | 💾 Model Size: 8.38 GB\n","\n","📂 Category: 📦 9-Uncensored_Models | 🗄️ Total Folder Size: 59.32 GB\n","--------------------------------------------------------------------------------\n"," └── 🛠️ Subfolder: Quality/Code | 🗂️ Folder Size: 17.28 GB\n"," 🔹 Model: Huihui_Qwen3_VL_30B_Uncensored_Q4_K_M.gguf | 💾 Model Size: 17.28 GB\n"," └── 🛠️ Subfolder: Quality/General | 🗂️ Folder Size: 20.22 GB\n"," 🔹 Model: Huihui_Qwen3.6_35B_Claude4.7_Opus_MTP_Uncensored_Q4_K.gguf | 💾 Model Size: 20.22 GB\n"," └── 🛠️ Subfolder: Speed/Code | 🗂️ Folder Size: 13.70 GB\n"," 🔹 Model: Huihui_Qwen3_Coder_30B_Uncensored_i1_Q3_K_M.gguf | 💾 Model Size: 13.70 GB\n"," └── 🛠️ Subfolder: Speed/General | 🗂️ Folder Size: 8.11 GB\n"," 🔹 Model: Huihui_Qwen3_VL_8B_Uncensored_Q8_0.gguf | 💾 Model Size: 8.11 GB\n","\n","==========================================================================================\n","🏁 SCAN COMPLETED!\n","📊 Total Unique Models Identified: 40\n","🗄️ Total Toolkit Storage Size (RAR Archives Total): 308.38 GB\n","==========================================================================================\n","and also i have gemma-4-31B-it-UD-Q4_K_XL.gguf , gemma-4-31B-mmproj-F16.gguf \n"," and\n","NAME ID SIZE MODIFIED\n","all-minilm:l6-v2 1b226e2802db 45 MB 4 months ago\n","translategemma:12b c2f9a9ca1ec7 8.1 GB 5 months ago\n","gpt-oss:20b 17052f91a42e 13 GB 5 months ago\n","gpt-oss-20b-local:latest 8c4347bf41d2 12 GB 5 months ago\n","qwen3:14b bdbd181c33f2 9.3 GB 10 months ago\n"]}]},{"cell_type":"markdown","source":["# New Section"],"metadata":{"id":"l_3jn48JWE5l"}},{"cell_type":"code","source":["import os\n","import re\n","import glob\n","\n","# ۱. تنظیم مسیر اصلی جعبه‌ابزار شما\n","BASE_PATH = \"/content/drive/MyDrive/Local AI Toolkit\"\n","\n","def get_readable_size(size_in_bytes):\n"," for unit in ['Bytes', 'KB', 'MB', 'GB', 'TB']:\n"," if size_in_bytes < 1024.0:\n"," return f\"{size_in_bytes:.2f} {unit}\"\n"," size_in_bytes /= 1024.0\n"," return f\"{size_in_bytes:.2f} PB\"\n","\n","print(\"=\"*90)\n","print(\"🔍 [LOCAL AI TOOLKIT - GHOST FILE AUDITOR & RADAR]\")\n","print(\"🎯 Mission: Detect duplicate standalone RARs matching Part1 byte-for-byte\")\n","print(\"=\"*90)\n","\n","if not os.path.exists(BASE_PATH):\n"," print(f\"❌ Error: Base path '{BASE_PATH}' not found. Verify your Drive mount.\")\n","else:\n"," total_wasted_space = 0\n"," ghost_files_found = 0\n","\n"," # اسکن عمیق و بازگشتی تمام پوشه‌ها برای پیدا کردن فایل‌های RAR\n"," for root, dirs, files in os.walk(BASE_PATH):\n"," rar_files = [f for f in files if f.endswith('.rar')]\n"," if not rar_files:\n"," continue\n","\n"," # ساختار داده برای گروه‌بندی فایل‌ها بر اساس نام پایه مدل\n"," model_groups = {}\n","\n"," for f in rar_files:\n"," # تشخیص اینکه آیا فایل پارت‌بندی شده است یا خیر\n"," is_part = re.search(r'\\.part(\\d+)\\.rar$', f)\n"," if is_part:\n"," part_num = int(is_part.group(1))\n"," base_name = re.sub(r'\\.part\\d+\\.rar$', '', f)\n"," if base_name not in model_groups:\n"," model_groups[base_name] = {'standalone': None, 'parts': {}}\n"," model_groups[base_name]['parts'][part_num] = f\n"," else:\n"," base_name = re.sub(r'\\.rar$', '', f)\n"," if base_name not in model_groups:\n"," model_groups[base_name] = {'standalone': None, 'parts': {}}\n"," model_groups[base_name]['standalone'] = f\n","\n"," # بررسی تداخل‌ها و مقایسه بایت به بایت مدل‌های یافت شده در این پوشه\n"," folder_header_printed = False\n","\n"," for model_name, data in model_groups.items():\n"," standalone = data['standalone']\n"," part1 = data['parts'].get(1)\n","\n"," # شرط اصلی: وجود هم‌زمان فایل خام RAR و فایل part1.rar\n"," if standalone and part1:\n"," standalone_path = os.path.join(root, standalone)\n"," part1_path = os.path.join(root, part1)\n","\n"," # محاسبه حجم دقیق فیزیکی بر حسب بایت\n"," standalone_size = os.path.getsize(standalone_path)\n"," part1_size = os.path.getsize(part1_path)\n","\n"," # مقایسه میلی‌متری حجم دو فایل\n"," if standalone_size == part1_size:\n"," ghost_files_found += 1\n"," total_wasted_space += standalone_size\n","\n"," # چاپ هدر پوشه فقط در صورت پیدا شدن باگ\n"," if not folder_header_printed:\n"," relative_path = os.path.relpath(root, BASE_PATH)\n"," print(f\"\\n📂 Location: 📦 Local AI Toolkit/{relative_path}\")\n"," print(\"-\" * 85)\n"," folder_header_printed = True\n","\n"," print(f\"🚨 GHOST DUPLICATE DETECTED:\")\n"," print(f\" ├─ 🛑 Ghost File : {standalone} ({get_readable_size(standalone_size)})\")\n"," print(f\" ├─ ✅ Valid Part1: {part1} ({get_readable_size(part1_size)})\")\n"," print(f\" └─ 📢 Verdict : ❌ CRITICAL BUG! Both files are identical bytes. DO NOT DOWNLOAD the Ghost File.\")\n","\n"," # گزارش نهایی سیستم\n"," print(\"\\n\" + \"=\"*90)\n"," print(\"🏁 AUDIT MATRIX COMPLETED!\")\n"," print(f\"📊 Total Duplicate Ghost Files Identified: {ghost_files_found}\")\n"," print(f\"🛑 Total Wasted Cloud Storage Identified : {get_readable_size(total_wasted_space)}\")\n"," if ghost_files_found > 0:\n"," print(\"💡 Action: Download ONLY the .partX.rar sequence. The standalone .rars listed above are safe to delete from Google Drive.\")\n"," else:\n"," print(\"✨ Clean Slate! No ghost duplicates or structural naming bugs detected in your current toolkit layout.\")\n"," print(\"=\"*90)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"__RRlSkgWFgT","executionInfo":{"status":"ok","timestamp":1783306432378,"user_tz":-210,"elapsed":4327,"user":{"displayName":"Aditya Kumar sharma","userId":"13769898468619597844"}},"outputId":"ef25ac48-97bc-43b8-845a-9b1c1fb10329"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["==========================================================================================\n","🔍 [LOCAL AI TOOLKIT - GHOST FILE AUDITOR & RADAR]\n","🎯 Mission: Detect duplicate standalone RARs matching Part1 byte-for-byte\n","==========================================================================================\n","\n","📂 Location: 📦 Local AI Toolkit/1-Universal_Foundation_LLMs/Quality\n","-------------------------------------------------------------------------------------\n","🚨 GHOST DUPLICATE DETECTED:\n"," ├─ 🛑 Ghost File : Qwen3.6-35B-A3B-UD-Q4_K_XL.rar (4.00 GB)\n"," ├─ ✅ Valid Part1: Qwen3.6-35B-A3B-UD-Q4_K_XL.part1.rar (4.00 GB)\n"," └─ 📢 Verdict : ❌ CRITICAL BUG! Both files are identical bytes. DO NOT DOWNLOAD the Ghost File.\n","\n","📂 Location: 📦 Local AI Toolkit/1-Universal_Foundation_LLMs/Speed\n","-------------------------------------------------------------------------------------\n","🚨 GHOST DUPLICATE DETECTED:\n"," ├─ 🛑 Ghost File : Qwen3.5-9B-Q5_K_M.rar (4.00 GB)\n"," ├─ ✅ Valid Part1: Qwen3.5-9B-Q5_K_M.part1.rar (4.00 GB)\n"," └─ 📢 Verdict : ❌ CRITICAL BUG! Both files are identical bytes. DO NOT DOWNLOAD the Ghost File.\n","\n","==========================================================================================\n","🏁 AUDIT MATRIX COMPLETED!\n","📊 Total Duplicate Ghost Files Identified: 2\n","🛑 Total Wasted Cloud Storage Identified : 8.00 GB\n","💡 Action: Download ONLY the .partX.rar sequence. The standalone .rars listed above are safe to delete from Google Drive.\n","==========================================================================================\n"]}]},{"cell_type":"markdown","source":["# New Section"],"metadata":{"id":"nnrRm29qWGES"}},{"cell_type":"code","source":["# ==============================================================================\n","# 🚀 LOCAL AI TOOLKIT: UNCENSORED MULTI-MODEL AUTOMATED DOWNLOADER & PACKAGER\n","# ==============================================================================\n","# Target Hardware: RTX 3060 12GB VRAM + 32GB RAM | Split-RAR Architecture (4GB)\n","# Root Directory: 📦 9-Uncensored_Models\n","# Updated Component: Quality/General upgraded to stable Q4_K (21.7 GB)\n","# ==============================================================================\n","\n","import os\n","import glob\n","import shutil\n","import hashlib\n","from huggingface_hub import hf_hub_download\n","\n","# پیکربندی ثابت‌ها\n","HF_TOKEN = \"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\"\n","PART_SIZE = \"4g\"\n","ROOT_DRIVE_PATH = \"/content/drive/MyDrive/Local AI Toolkit/9-Uncensored_Models\"\n","\n","# کانفیگ هوشمند توزیع ۴ مدل در ساختار درختی سریع/کیفیت و کد/عمومی\n","MODELS_DATASET = [\n"," {\n"," \"category\": \"Speed/General\",\n"," \"repo_id\": \"mradermacher/Huihui-Qwen3-VL-8B-Instruct-abliterated-GGUF\",\n"," \"target_file\": \"Huihui-Qwen3-VL-8B-Instruct-abliterated.Q8_0.gguf\",\n"," \"pack_name\": \"Huihui_Qwen3_VL_8B_Uncensored_Q8_0\"\n"," },\n"," {\n"," \"category\": \"Speed/Code\",\n"," \"repo_id\": \"mradermacher/Huihui-Qwen3-Coder-30B-A3B-Instruct-abliterated-i1-GGUF\",\n"," \"target_file\": \"Huihui-Qwen3-Coder-30B-A3B-Instruct-abliterated.i1-Q3_K_M.gguf\",\n"," \"pack_name\": \"Huihui_Qwen3_Coder_30B_Uncensored_i1_Q3_K_M\"\n"," },\n"," {\n"," \"category\": \"Quality/General\",\n"," \"repo_id\": \"huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-MTP-GGUF\",\n"," \"target_file\": \"Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-ggml-model-Q4_K.gguf\",\n"," \"pack_name\": \"Huihui_Qwen3.6_35B_Claude4.7_Opus_MTP_Uncensored_Q4_K\"\n"," },\n"," {\n"," \"category\": \"Quality/Code\",\n"," \"repo_id\": \"mradermacher/Huihui-Qwen3-VL-30B-A3B-Instruct-abliterated-GGUF\",\n"," \"target_file\": \"Huihui-Qwen3-VL-30B-A3B-Instruct-abliterated.Q4_K_M.gguf\",\n"," \"pack_name\": \"Huihui_Qwen3_VL_30B_Uncensored_Q4_K_M\"\n"," }\n","]\n","\n","def calculate_sha256(filepath):\n"," \"\"\"محاسبه دقیق هش باینری فایل برای تضمین صحت فشرده‌سازی\"\"\"\n"," sha256_hash = hashlib.sha256()\n"," with open(filepath, \"rb\") as f:\n"," for byte_block in iter(lambda: f.read(65536), b\"\"):\n"," sha256_hash.update(byte_block)\n"," return sha256_hash.hexdigest()\n","\n","print(\"🔗 [1/6] Initializing dependencies and securing environment...\")\n","os.system('apt-get update > /dev/null && apt-get install unrar rar -y > /dev/null')\n","os.system('pip install huggingface_hub tqdm > /dev/null')\n","\n","# اجرای پایپ‌لاین برای هر ۴ مدل به صورت ترتیبی\n","for idx, model in enumerate(MODELS_DATASET, 1):\n"," print(\"\\n\" + \"═\"*90)\n"," print(f\"🔄 PROCESSING MODEL {idx}/4: {model['target_file']}\")\n"," print(f\"📂 Destination Category: {model['category']}\")\n"," print(\"═\"*90)\n","\n"," # ساخت دایرکتوری نهایی روی گوگل درایو\n"," target_drive_folder = os.path.join(ROOT_DRIVE_PATH, model['category'])\n"," os.makedirs(target_drive_folder, exist_ok=True)\n","\n"," # تعیین مسیرهای موقت روی دیسک لوکال کولب\n"," local_model_dir = f\"/content/raw_{model['pack_name']}\"\n"," verify_extract_dir = f\"/content/verify_{model['pack_name']}\"\n"," os.makedirs(local_model_dir, exist_ok=True)\n","\n"," try:\n"," # ۲. دانلود مدل از هافینگ‌فیس\n"," print(f\"📥 [2/6] Downloading asset from HuggingFace ({model['repo_id']})...\")\n"," hf_hub_download(\n"," repo_id=model['repo_id'],\n"," filename=model['target_file'],\n"," local_dir=local_model_dir,\n"," token=HF_TOKEN\n"," )\n","\n"," # یکپارچه‌سازی آدرس فایل دانلود شده\n"," search_pattern = os.path.join(local_model_dir, \"**\", model['target_file'])\n"," discovered_files = glob.glob(search_pattern, recursive=True)\n","\n"," if not discovered_files:\n"," raise FileNotFoundError(f\"Downloaded file {model['target_file']} not found in cache layout.\")\n","\n"," original_file_path = discovered_files[0]\n","\n"," # محاسبه هش فایل اصلی\n"," print(\"🧬 Calculating SHA-256 cryptographic signature of original file...\")\n"," original_hash = calculate_sha256(original_file_path)\n"," print(f\" Original Hash: {original_hash}\")\n"," print(\"-\" * 70)\n","\n"," # ۳. بسته‌بندی پارت‌بندی شده RAR روی گوگل درایو\n"," print(f\"📦 [3/6] Packaging asset into {PART_SIZE} split-RAR volumes on Google Drive...\")\n"," rar_output_base = os.path.join(target_drive_folder, model['pack_name'])\n"," rar_cmd = f'rar a -m0 -r -v{PART_SIZE} \"{rar_output_base}.rar\" \"{original_file_path}\" > /dev/null'\n"," os.system(rar_cmd)\n"," print(\"✅ Segmented RAR packages generated successfully.\")\n"," print(\"-\" * 70)\n","\n"," # ۴. تست فیزیکی سلامت پارت‌ها با اکسترکت آزمایشی\n"," print(\"📥 [4/6] Extracting RAR volumes to verify container integrity...\")\n"," os.makedirs(verify_extract_dir, exist_ok=True)\n"," part_files = sorted(glob.glob(f\"{rar_output_base}.part*.rar\"))\n"," single_path = f\"{rar_output_base}.rar\"\n"," target_rar_to_open = part_files[0] if part_files else single_path\n","\n"," unrar_cmd = f'unrar x \"{target_rar_to_open}\" \"{verify_extract_dir}/\" > /dev/null'\n"," os.system(unrar_cmd)\n"," print(\"✅ Extraction verification sequence finished.\")\n"," print(\"-\" * 70)\n","\n"," # ۵. تایید هش نهایی\n"," print(\"🧪 [5/6] Comparing hashes to guarantee bit-perfect extraction...\")\n"," extracted_file_search = glob.glob(os.path.join(verify_extract_dir, \"**\", model['target_file']), recursive=True)\n","\n"," if extracted_file_search:\n"," extracted_hash = calculate_sha256(extracted_file_search[0])\n"," print(f\" Extracted Hash: {extracted_hash}\")\n","\n"," if original_hash == extracted_hash:\n"," print(f\"\\n🎉 SUCCESS: {model['target_file']} is 100% HEALTHY and locked in Drive!\")\n"," else:\n"," print(f\"\\n🚨 CRITICAL ERROR: Integrity mismatch for {model['target_file']}!\")\n"," raise ValueError(\"Pack integrity compromised! Hashes do not match.\")\n"," else:\n"," raise FileNotFoundError(\"Extracted file missing during validation stage.\")\n","\n"," except Exception as e:\n"," print(f\"❌ Core Process Interrupted for {model['target_file']}: {e}\")\n","\n"," finally:\n"," # ۶. پاک‌سازی دیسک لوکال برای جلوگیری از پر شدن هارد کولب در دانلود بعدی\n"," shutil.rmtree(local_model_dir, ignore_errors=True)\n"," shutil.rmtree(verify_extract_dir, ignore_errors=True)\n"," print(f\"🧹 [6/6] Flushed local temporary cache for {model['pack_name']}.\")\n","\n","print(\"\\n\" + \"═\"*90)\n","print(\"🏁 ALL TASKS COMPLETED: Uncensored category is populated with 4 flagship models!\")\n","print(\"═\"*90)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["555a22b17e394edd9e414ab5c5e24042","eb621e04c1524163bbb56a9b005fed2d","bec23408c7b3441198876856a84f8543","3cd2be8ed7244785aee1e54d99d37be1","2b2a84df181746f6b342ee12a1d7d21e","23941c8300ce40afab57c47227bc27b0","c4c85b1df0194a35a56153e1eef13d35","02bcfd5d6bb040858777cbf7fbf9119d","edd85393f3c04551ab63296ccca4e3a5","fd19b03029934671877a488455141386","2475d109a59a48b1af4f421b3705dc8c","28d694a6767445a2b729bfd926016926","d03e46c610dd46aabd3bc2827bff800c","9e87adae26014987ad20548b68173b36","aa93a701478f44a99b8c7b58952a16d2","78088d0987494a57b94aee90e67e94ad","d2940487aac14649a2e69dfdedcae7c1","263e16b298b84ce5bd9e080f16f37b89","687282995b8a4554a654f3660089fc48","88e11f2423534726b316ff0a40968c49","fc7f4d205cf448ada80bd84aacb1a5ed","33499d066ce245c4ad659ff3df0ca0e3","f37f5b5b6da34234b9d3c1b2bff76196","11492f4699834fc588c04063a5747afb","e9e6b4cfccaa47a9a09e59c65c555d97","62171df4f47d475ba7cd603c8790ca9c","874fcd2ffece40a1be5328b0aa617ce7","ed7a454ecb0c41859171eca310daccdf","f716c82d21a542c793107190f8af84d2","ef0688331125497193b41ded01240e96","da1b99d4bd88455c9aa82fbdeb8c8e7c","6db2293b53fb43b0bc230151a33b712d","58b4d61ae2ed449db1d79c6cd700a967","501e67c3347f46dc89cd8ec08415161d","1249cc9718bd4eceb290fbebd428a2db","e51f464fb4dd4d96b690318d00b267da","7c3da0132f6e4700995e0fb2072f31e5","31adfd40a5514c11b5519d7291fae91c","e13412fbd8bf4fb2a3746de5527c1efb","7c84cc34b2384bc794efd9636daa9e44","a65e7dc2a301439da53a7ec9ba93e232","655969e4de2e423aa6e36b780abdc6ce","79b0251f52c944fd823fe9ad9ad8472b","8bc798d8d233438f94adcfdb9319b36e"]},"id":"aEsuiqGJKFU3","executionInfo":{"status":"ok","timestamp":1783407186462,"user_tz":-210,"elapsed":3256762,"user":{"displayName":"Aditya Kumar sharma","userId":"13769898468619597844"}},"outputId":"d0c5e8c6-b1ae-4d0f-90bb-5e37f1e37f4c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["🔗 [1/6] Initializing dependencies and securing environment...\n","\n","══════════════════════════════════════════════════════════════════════════════════════════\n","🔄 PROCESSING MODEL 1/4: Huihui-Qwen3-VL-8B-Instruct-abliterated.Q8_0.gguf\n","📂 Destination Category: Speed/General\n","══════════════════════════════════════════════════════════════════════════════════════════\n","📥 [2/6] Downloading asset from HuggingFace (mradermacher/Huihui-Qwen3-VL-8B-Instruct-abliterated-GGUF)...\n"]},{"output_type":"display_data","data":{"text/plain":["Huihui-Qwen3-VL-8B-Instruct-abliterated.(…): 0%| | 0.00/8.71G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"555a22b17e394edd9e414ab5c5e24042"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["🧬 Calculating SHA-256 cryptographic signature of original file...\n"," Original Hash: 2a80cab9a07998164351e47c67380cbd10df95afbe8f3726688b82723689c64c\n","----------------------------------------------------------------------\n","📦 [3/6] Packaging asset into 4g split-RAR volumes on Google Drive...\n","✅ Segmented RAR packages generated successfully.\n","----------------------------------------------------------------------\n","📥 [4/6] Extracting RAR volumes to verify container integrity...\n","✅ Extraction verification sequence finished.\n","----------------------------------------------------------------------\n","🧪 [5/6] Comparing hashes to guarantee bit-perfect extraction...\n"," Extracted Hash: 2a80cab9a07998164351e47c67380cbd10df95afbe8f3726688b82723689c64c\n","\n","🎉 SUCCESS: Huihui-Qwen3-VL-8B-Instruct-abliterated.Q8_0.gguf is 100% HEALTHY and locked in Drive!\n","🧹 [6/6] Flushed local temporary cache for Huihui_Qwen3_VL_8B_Uncensored_Q8_0.\n","\n","══════════════════════════════════════════════════════════════════════════════════════════\n","🔄 PROCESSING MODEL 2/4: Huihui-Qwen3-Coder-30B-A3B-Instruct-abliterated.i1-Q3_K_M.gguf\n","📂 Destination Category: Speed/Code\n","══════════════════════════════════════════════════════════════════════════════════════════\n","📥 [2/6] Downloading asset from HuggingFace (mradermacher/Huihui-Qwen3-Coder-30B-A3B-Instruct-abliterated-i1-GGUF)...\n"]},{"output_type":"display_data","data":{"text/plain":["Huihui-Qwen3-Coder-30B-A3B-Instruct-abli(…): 0%| | 0.00/14.7G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"28d694a6767445a2b729bfd926016926"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["🧬 Calculating SHA-256 cryptographic signature of original file...\n"," Original Hash: 00e4bd529eb749e8eb3397fb9b20d3fedcc63ac82fbed238c7c7ef22c23facd7\n","----------------------------------------------------------------------\n","📦 [3/6] Packaging asset into 4g split-RAR volumes on Google Drive...\n","✅ Segmented RAR packages generated successfully.\n","----------------------------------------------------------------------\n","📥 [4/6] Extracting RAR volumes to verify container integrity...\n","✅ Extraction verification sequence finished.\n","----------------------------------------------------------------------\n","🧪 [5/6] Comparing hashes to guarantee bit-perfect extraction...\n"," Extracted Hash: 00e4bd529eb749e8eb3397fb9b20d3fedcc63ac82fbed238c7c7ef22c23facd7\n","\n","🎉 SUCCESS: Huihui-Qwen3-Coder-30B-A3B-Instruct-abliterated.i1-Q3_K_M.gguf is 100% HEALTHY and locked in Drive!\n","🧹 [6/6] Flushed local temporary cache for Huihui_Qwen3_Coder_30B_Uncensored_i1_Q3_K_M.\n","\n","══════════════════════════════════════════════════════════════════════════════════════════\n","🔄 PROCESSING MODEL 3/4: Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-ggml-model-Q4_K.gguf\n","📂 Destination Category: Quality/General\n","══════════════════════════════════════════════════════════════════════════════════════════\n","📥 [2/6] Downloading asset from HuggingFace (huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-MTP-GGUF)...\n"]},{"output_type":"display_data","data":{"text/plain":["Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-a(…): 0%| | 0.00/21.7G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f37f5b5b6da34234b9d3c1b2bff76196"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["🧬 Calculating SHA-256 cryptographic signature of original file...\n"," Original Hash: a20002fdac5d529946ef2ab3a4ad5da953e77ca7e30dcc6ca9b6b738e0c7ff4d\n","----------------------------------------------------------------------\n","📦 [3/6] Packaging asset into 4g split-RAR volumes on Google Drive...\n","✅ Segmented RAR packages generated successfully.\n","----------------------------------------------------------------------\n","📥 [4/6] Extracting RAR volumes to verify container integrity...\n","✅ Extraction verification sequence finished.\n","----------------------------------------------------------------------\n","🧪 [5/6] Comparing hashes to guarantee bit-perfect extraction...\n"," Extracted Hash: a20002fdac5d529946ef2ab3a4ad5da953e77ca7e30dcc6ca9b6b738e0c7ff4d\n","\n","🎉 SUCCESS: Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-ggml-model-Q4_K.gguf is 100% HEALTHY and locked in Drive!\n","🧹 [6/6] Flushed local temporary cache for Huihui_Qwen3.6_35B_Claude4.7_Opus_MTP_Uncensored_Q4_K.\n","\n","══════════════════════════════════════════════════════════════════════════════════════════\n","🔄 PROCESSING MODEL 4/4: Huihui-Qwen3-VL-30B-A3B-Instruct-abliterated.Q4_K_M.gguf\n","📂 Destination Category: Quality/Code\n","══════════════════════════════════════════════════════════════════════════════════════════\n","📥 [2/6] Downloading asset from HuggingFace (mradermacher/Huihui-Qwen3-VL-30B-A3B-Instruct-abliterated-GGUF)...\n"]},{"output_type":"display_data","data":{"text/plain":["Huihui-Qwen3-VL-30B-A3B-Instruct-abliter(…): 0%| | 0.00/18.6G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"501e67c3347f46dc89cd8ec08415161d"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["🧬 Calculating SHA-256 cryptographic signature of original file...\n"," Original Hash: 157808caa031a7bb70451730afb8229c2c51bbfe573fa1e6f5cf64eccfca9f74\n","----------------------------------------------------------------------\n","📦 [3/6] Packaging asset into 4g split-RAR volumes on Google Drive...\n","✅ Segmented RAR packages generated successfully.\n","----------------------------------------------------------------------\n","📥 [4/6] Extracting RAR volumes to verify container integrity...\n","✅ Extraction verification sequence finished.\n","----------------------------------------------------------------------\n","🧪 [5/6] Comparing hashes to guarantee bit-perfect extraction...\n"," Extracted Hash: 157808caa031a7bb70451730afb8229c2c51bbfe573fa1e6f5cf64eccfca9f74\n","\n","🎉 SUCCESS: Huihui-Qwen3-VL-30B-A3B-Instruct-abliterated.Q4_K_M.gguf is 100% HEALTHY and locked in Drive!\n","🧹 [6/6] Flushed local temporary cache for Huihui_Qwen3_VL_30B_Uncensored_Q4_K_M.\n","\n","══════════════════════════════════════════════════════════════════════════════════════════\n","🏁 ALL TASKS COMPLETED: Uncensored category is populated with 4 flagship models!\n","══════════════════════════════════════════════════════════════════════════════════════════\n"]}]},{"cell_type":"code","source":["import hashlib\n","\n","def get_file_sha256(file_path):\n"," sha256_hash = hashlib.sha256()\n"," print(hashlib.sha256())\n"," with open(file_path, \"rb\") as f:\n"," # فایل را تکه تکه می‌خوانیم تا رم کولب پر نشود\n"," for byte_block in iter(lambda: f.read(4096), b\"\"):\n"," sha256_hash.update(byte_block)\n"," return sha256_hash.hexdigest()\n","\n","path1 = \"/content/drive/MyDrive/Local AI Toolkit/7-Multimodal/Speed/Gemma4_E4B_Speed_Speculative.part1.rar\"\n","path2 = \"/content/drive/MyDrive/Local AI Toolkit/7-Multimodal/Speed/Gemma4_E4B_Speed_Speculative.part2.rar\"\n","\n","print(\"Colab Part 1 Hash:\", get_file_sha256(path1))\n","print(\"Colab Part 2 Hash:\", get_file_sha256(path2))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nluZqfNNO4_h","executionInfo":{"status":"ok","timestamp":1783407293054,"user_tz":-210,"elapsed":106239,"user":{"displayName":"Aditya Kumar sharma","userId":"13769898468619597844"}},"outputId":"969af325-e513-44ab-b300-aeada8f9501b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<sha256 _hashlib.HASH object @ 0x7d6bc7cedf70>\n","Colab Part 1 Hash: 3d1cc567be24191c1e3e0acdb8a7e7a312ceafd80fcba911ab6abbad911b33b4\n","<sha256 _hashlib.HASH object @ 0x7d6bc7cedeb0>\n","Colab Part 2 Hash: 6de6d2f16e6c880d1dfbd366dde95f00dc9ba4286bacfaa2e9f3118636e1ee3a\n"]}]},{"cell_type":"code","source":["# ۱. متصل کردن گوگل درایو\n","from google.colab import drive\n","\n","import os\n","import requests\n","from tqdm.notebook import tqdm\n","\n","# ۲. تعریف دقیق مسیر و ایجاد پوشه TEST (در صورت عدم وجود)\n","# پایتون فاصله‌های موجود در \"درس های دانشگاه\" را به درستی پردازش می‌کند\n","target_folder = \"/content/drive/MyDrive/mohammad/درس های دانشگاه/TEST\"\n","os.makedirs(target_folder, exist_ok=True)\n","\n","# ۳. مشخص کردن لینک دانلود و نام فایل نهایی\n","video_url = \"http://www.archive.org/download/MIT5.111F08/ocw-5.111-f08-lec25_300k.mp4\"\n","file_name = \"Lecture 25: Electrochemical Cells.mp4\"\n","full_path = os.path.join(target_folder, file_name)\n","\n","# ۴. دانلود ایمن و مستقیم با پایتون همراه با نوار پیشرفت درصد\n","print(\"در حال دانلود ویدیو... لطفا شکیبا باشید.\")\n","try:\n"," response = requests.get(video_url, stream=True)\n"," response.raise_for_status() # اطمینان از سلامت لینک\n"," total_size = int(response.headers.get('content-length', 0))\n"," block_size = 1024 * 1024 # دانلود در پارت‌های ۱ مگابایتی برای سرعت بیشتر\n","\n"," with open(full_path, 'wb') as file, tqdm(\n"," desc=\"درصد پیشرفت\",\n"," total=total_size,\n"," unit='B',\n"," unit_scale=True,\n"," unit_divisor=1024,\n"," ) as bar:\n"," for data in response.iter_content(block_size):\n"," file.write(data)\n"," bar.update(len(data))\n","\n"," print(f\"\\nدانلود با موفقیت انجام شد! فایل در مسیر زیر ذخیره گردید:\\n{full_path}\")\n","except Exception as e:\n"," print(f\"\\nخطایی در حین دانلود رخ داد: {e}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":118,"referenced_widgets":["a420bd81cf934902b5a155a6e365e6f5","796d20bf01814d05b95295ec5243ea51","fb27fec6b770462a873395f8d0960c84","2d18c34f1c2d4cee949d60038f51f193","243fb0e5b5954d4880ad7a6667e7f55b","8985fe10cf844c868036873f671bf186","1614e5b8365b4f3eafeafbd0084b7123","c3621ddb2375494693d2491333de6295","8124a79fe0ff47b2b0504e6dd4eedccf","34ea357c441c40c3a0d64e1f70cbebb7","59d7d0bb65774690bae46e098f47a93d"]},"id":"wW6cNEQVgigE","executionInfo":{"status":"ok","timestamp":1784114883563,"user_tz":-210,"elapsed":10812,"user":{"displayName":"Aditya Kumar sharma","userId":"13769898468619597844"}},"outputId":"1d254032-0c6a-4797-de87-4cae51387385"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["در حال دانلود ویدیو... لطفا شکیبا باشید.\n"]},{"output_type":"display_data","data":{"text/plain":["درصد پیشرفت: 0%| | 0.00/107M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a420bd81cf934902b5a155a6e365e6f5"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","دانلود با موفقیت انجام شد! فایل در مسیر زیر ذخیره گردید:\n","/content/drive/MyDrive/mohammad/درس های دانشگاه/TEST/Lecture 25: Electrochemical Cells.mp4\n"]}]}]}