Text Generation
GGUF
Russian
English
lora
ollama
gemma
research-preview
conversational-ai
engineering-coauthor
conversational
Instructions to use 0penAGI/0pen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use 0penAGI/0pen 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 0penAGI/0pen # Run inference directly in the terminal: llama cli -hf 0penAGI/0pen
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf 0penAGI/0pen # Run inference directly in the terminal: llama cli -hf 0penAGI/0pen
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 0penAGI/0pen # Run inference directly in the terminal: ./llama-cli -hf 0penAGI/0pen
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 0penAGI/0pen # Run inference directly in the terminal: ./build/bin/llama-cli -hf 0penAGI/0pen
Use Docker
docker model run hf.co/0penAGI/0pen
- LM Studio
- Jan
- vLLM
How to use 0penAGI/0pen with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "0penAGI/0pen" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "0penAGI/0pen", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/0penAGI/0pen
- Ollama
How to use 0penAGI/0pen with Ollama:
ollama run hf.co/0penAGI/0pen
- Unsloth Studio
How to use 0penAGI/0pen 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 0penAGI/0pen 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 0penAGI/0pen to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for 0penAGI/0pen to start chatting
- Atomic Chat new
- Docker Model Runner
How to use 0penAGI/0pen with Docker Model Runner:
docker model run hf.co/0penAGI/0pen
- Lemonade
How to use 0penAGI/0pen with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull 0penAGI/0pen
Run and chat with the model
lemonade run user.0pen-{{QUANT_TAG}}List all available models
lemonade list
| { | |
| "adapter_path": "zephyr_lora_agr_v1", | |
| "agr": true, | |
| "agr_centers": 32, | |
| "agr_checkpoint": null, | |
| "agr_ema": 0.99, | |
| "agr_lambda": 0.01, | |
| "agr_min_dist": 1.0, | |
| "agr_weight": 0.01, | |
| "batch_size": 1, | |
| "clear_cache_threshold": 0, | |
| "config": null, | |
| "data": "data_zephyr_enhanced", | |
| "fine_tune_type": "lora", | |
| "grad_accumulation_steps": 1, | |
| "grad_checkpoint": true, | |
| "iters": 4000, | |
| "learning_rate": 1e-05, | |
| "lora_parameters": { | |
| "rank": 8, | |
| "dropout": 0.0, | |
| "scale": 20.0 | |
| }, | |
| "lr_schedule": null, | |
| "mask_prompt": true, | |
| "max_seq_length": 1792, | |
| "model": "./gemma4-e4b-mlx", | |
| "num_layers": 12, | |
| "optimizer": "adam", | |
| "optimizer_config": { | |
| "adam": {}, | |
| "adamw": {}, | |
| "muon": {}, | |
| "sgd": {}, | |
| "adafactor": {} | |
| }, | |
| "project_name": null, | |
| "report_to": null, | |
| "resume_adapter_file": null, | |
| "save_every": 200, | |
| "seed": 0, | |
| "steps_per_eval": 100, | |
| "steps_per_report": 10, | |
| "test": false, | |
| "test_batches": 500, | |
| "train": true, | |
| "val_batches": 4 | |
| } |