Text Generation
GGUF
Russian
English
lora
ollama
gemma
conversational-ai
engineering-coauthor
Vision
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 Desktop
- 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
- Atomic Chat
Upload README.md with huggingface_hub
Browse files
README.md
CHANGED
|
@@ -22,6 +22,8 @@ tags:
|
|
| 22 |
|
| 23 |
This is the first public release of an ongoing research project: the dataset was written from scratch, the LoRA was trained locally, and the architecture was modified and experimented with. The model is distributed as a **GGUF (Q4_K_M)** file ready to run in [Ollama](https://ollama.com).
|
| 24 |
|
|
|
|
|
|
|
| 25 |
## Why this model exists
|
| 26 |
|
| 27 |
Most fine-tuned models are optimized for one thing: following instructions well. 0pen is interested in something slightly different — being a **consistent presence in a conversation**. That means:
|
|
|
|
| 22 |
|
| 23 |
This is the first public release of an ongoing research project: the dataset was written from scratch, the LoRA was trained locally, and the architecture was modified and experimented with. The model is distributed as a **GGUF (Q4_K_M)** file ready to run in [Ollama](https://ollama.com).
|
| 24 |
|
| 25 |
+
**Source code & full release:** [github.com/0penAGI/0pen](https://github.com/0penAGI/0pen) — dataset pipeline, AGR training wrapper, adapter weights, and scripts.
|
| 26 |
+
|
| 27 |
## Why this model exists
|
| 28 |
|
| 29 |
Most fine-tuned models are optimized for one thing: following instructions well. 0pen is interested in something slightly different — being a **consistent presence in a conversation**. That means:
|