Instructions to use Ashok75/GAKR-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Ashok75/GAKR-model with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Ashok75/GAKR-model", filename="gakr-model-01.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Ashok75/GAKR-model with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Ashok75/GAKR-model # Run inference directly in the terminal: llama-cli -hf Ashok75/GAKR-model
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Ashok75/GAKR-model # Run inference directly in the terminal: llama-cli -hf Ashok75/GAKR-model
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Ashok75/GAKR-model # Run inference directly in the terminal: ./llama-cli -hf Ashok75/GAKR-model
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Ashok75/GAKR-model # Run inference directly in the terminal: ./build/bin/llama-cli -hf Ashok75/GAKR-model
Use Docker
docker model run hf.co/Ashok75/GAKR-model
- LM Studio
- Jan
- Ollama
How to use Ashok75/GAKR-model with Ollama:
ollama run hf.co/Ashok75/GAKR-model
- Unsloth Studio
How to use Ashok75/GAKR-model with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Ashok75/GAKR-model to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Ashok75/GAKR-model to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Ashok75/GAKR-model to start chatting
- Pi
How to use Ashok75/GAKR-model with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Ashok75/GAKR-model
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Ashok75/GAKR-model" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Ashok75/GAKR-model with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Ashok75/GAKR-model
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 Ashok75/GAKR-model
Run Hermes
hermes
- Docker Model Runner
How to use Ashok75/GAKR-model with Docker Model Runner:
docker model run hf.co/Ashok75/GAKR-model
- Lemonade
How to use Ashok75/GAKR-model with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Ashok75/GAKR-model
Run and chat with the model
lemonade run user.GAKR-model-{{QUANT_TAG}}List all available models
lemonade list
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
refer the space - ashok75-gakr.hf.space
GAKR AI โ Local FileโAware Chat Assistant
GAKR AI is a local, privacyโfriendly chat assistant that runs entirely on your machine.
It combines a FastAPI backend, a modern web chat UI, and a fileโintelligence pipeline that can read and summarize many file types before generating naturalโlanguage responses.
The assistant itself is textโonly. It never directly sees raw PDFs, images, audio, or videos.
Instead, specialized tools convert files into structured text summaries, and the language model reasons over that text.
โจ Features
๐ Web Chat Interface
- Clean dark UI with message bubbles and typing indicator
- Autoโgrowing input box
- Attach files from camera, gallery, or filesystem
- Works in any modern browser at http://localhost:8080
๐ง Text + File Understanding
- Prompt only โ general assistant (explanations, coding help, reasoning)
- Prompt + files โ full analysis pipeline:
- Detects file type
- Stores uploads in
dataupload/ - Extracts structured facts
- Feeds extracted context + question to the model
๐ MultiโFile, MultiโType Uploads
Upload multiple files at once:
- Documents: PDF, DOCX, TXT
- Tabular data: CSV, Excel, JSON
- Images: OCR via Tesseract
- Audio: Speechโtoโtext via Whisper
- Video: Audio extraction via ffmpeg โ Whisper
๐พ Persistent Uploads
- Files saved under
dataupload/by type - Timestamped, safe filenames
- Automatic directory creation
๐ Simple Login Reminder UX
- After 5 guest messages, a popup encourages login
- Loggedโin users are not interrupted
- Login state stored in
localStorage
๐ Project Structure
project_root/
โโโ run.py # FastAPI backend + template serving
โโโ load_model.py # Loads the language model once
โโโ generate.py # generate_response() wrapper
โโโ file_pipeline.py # File detection, storage, and summarization
โโโ templates/
โ โโโ chat.html # Main chat interface
โ โโโ auth.html # Login / signup UI
โโโ dataupload/ # Created at runtime for uploads
โ โโโ images/
โ โโโ videos/
โ โโโ audio/
โ โโโ documents/
โ โโโ tabular/
โ โโโ other/
โโโ requirements.txt
โ๏ธ Installation
1๏ธโฃ Create & Activate Virtual Environment (Recommended)
python -m venv .venv
source .venv/bin/activate # Linux / macOS
# or
.\.venv\Scripts\activate # Windows
2๏ธโฃ Install Python Dependencies
pip install -r requirements.txt
requirements.txt
fastapi
uvicorn[standard]
python-multipart
torch
transformers
accelerate
safetensors
pandas
numpy
pdfplumber
pymupdf
python-docx
Pillow
pytesseract
openai-whisper
ffmpeg-python
3๏ธโฃ Install System Tools
- Tesseract OCR (for image text extraction)
- ffmpeg (for audio extraction and Whisper)
Install via OS package manager (apt, brew, choco) or official installers.
โถ๏ธ Running GAKR AI
Start the Backend
python run.py
Expected output:
๐ Starting GAKR AI Backend...
โ
Model initialized successfully
๐ SERVER & CHAT LOCATION
๐ CHAT INTERFACE: http://localhost:8080
๐ง API DOCUMENTATION: http://localhost:8080/docs
โ
CHAT.HTML SERVED: templates/chat.html
Open the Chat UI
Navigate to:
http://localhost:8080
๐ API Overview
POST /api/analyze
Request (multipart/form-data)
api_key(string, required)prompt(string, required)files(optional, multiple)
Behavior
- No files โ General assistant mode
- With files โ Fileโanalysis mode using structured summaries
Response
{
"response": "natural-language answer here",
"context": {
"files": [
{
"original_name": "report.pdf",
"stored_path": "dataupload/documents/20241214_report.pdf",
"kind": "document",
"summary": {
"type": "document",
"char_count": 12345,
"preview": "First 4000 characters..."
}
}
]
},
"status": "success"
}
๐งช File Intelligence Pipeline
Handled by file_pipeline.py
Type Detection
- Tabular โ CSV, XLSX, JSON
- Documents โ PDF, DOCX, TXT
- Images โ PNG, JPG
- Audio โ MP3, WAV
- Video โ MP4, MKV
Summaries
- Tabular: rows, columns, missing values, stats
- Documents: character count + preview
- Images: dimensions + OCR text
- Audio: duration + transcript preview
- Video: extracted audio analysis
Errors are stored perโfile and never crash the whole request.
๐จ Frontend UX Highlights
- Autoโgrowing textarea
- Attachment chips with remove buttons
- Typing indicator
- URL prefill:
?q=your+question - Generic error message for all backend failures
๐ Security Notes
- API key is currently a fixed string (for local use)
- For production:
- Use environment variables
- Add real authentication (JWT / sessions)
- Restrict CORS
- Apply upload size limits and cleanup policies
๐ Extending GAKR AI
Ideas:
- Perโuser chat & file history (database)
- Search across uploaded documents
- External API integrations
- HTTPS + reverse proxy deployment
๐ง Philosophy
GAKR AI is an intelligence layer.
Tools translate reality (files, media, data) into structured language.
The language model turns that language into insight, reasoning, and action.
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docker model run hf.co/Ashok75/GAKR-model