Text Generation
Safetensors
GGUF
English
reasoning
reinforcement-learning
grpo
small-language-model
samsung-ennovatex
conversational
Instructions to use OmnipotentFool/Aurvion 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 OmnipotentFool/Aurvion 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 OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: llama cli -hf OmnipotentFool/Aurvion:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: llama cli -hf OmnipotentFool/Aurvion:Q4_K_M
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 OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf OmnipotentFool/Aurvion:Q4_K_M
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 OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf OmnipotentFool/Aurvion:Q4_K_M
Use Docker
docker model run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use OmnipotentFool/Aurvion with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OmnipotentFool/Aurvion" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OmnipotentFool/Aurvion", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- Ollama
How to use OmnipotentFool/Aurvion with Ollama:
ollama run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- Unsloth Studio
How to use OmnipotentFool/Aurvion 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 OmnipotentFool/Aurvion 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 OmnipotentFool/Aurvion to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for OmnipotentFool/Aurvion to start chatting
- Docker Model Runner
How to use OmnipotentFool/Aurvion with Docker Model Runner:
docker model run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- Lemonade
How to use OmnipotentFool/Aurvion with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull OmnipotentFool/Aurvion:Q4_K_M
Run and chat with the model
lemonade run user.Aurvion-Q4_K_M
List all available models
lemonade list
- Atomic Chat
| # llama.cpp/examples/debug | |
| This is a utility intended to help debug a model by registering a callback that | |
| logs GGML operations and tensor data. It can also store the generated logits or | |
| embeddings as well as the prompt and token ids for comparison with the original | |
| model. | |
| ### Usage | |
| ```shell | |
| llama-debug \ | |
| --hf-repo ggml-org/models \ | |
| --hf-file phi-2/ggml-model-q4_0.gguf \ | |
| --model phi-2-q4_0.gguf \ | |
| --prompt hello \ | |
| --save-logits \ | |
| --verbose | |
| ``` | |
| The tensor data is logged as debug and required the --verbose flag. The reason | |
| for this is that while useful for a model with many layers there can be a lot of | |
| output. You can filter the tensor names using the `--tensor-filter` option. | |
| A recommended approach is to first run without `--verbose` and see if the | |
| generated logits/embeddings are close to the original model. If they are not, | |
| then it might be required to inspect tensor by tensor and in that case it is | |
| useful to enable the `--verbose` flag along with `--tensor-filter` to focus on | |
| specific tensors. | |
| ### Options | |
| This example supports all standard `llama.cpp` options and also accepts the | |
| following options: | |
| ```console | |
| $ llama-debug --help | |
| ... | |
| ----- example-specific params ----- | |
| --save-logits save final logits to files for verification (default: false) | |
| --logits-output-dir PATH directory for saving logits output files (default: data) | |
| --tensor-filter REGEX filter tensor names for debug output (regex pattern, can be specified multiple times) | |
| ``` | |
| ### Output Files | |
| When `--save-logits` is enabled, the following files are created in the output | |
| directory: | |
| * `llamacpp-<model>[-embeddings].bin` - Binary output (logits or embeddings) | |
| * `llamacpp-<model>[-embeddings].txt` - Text output (logits or embeddings, one per line) | |
| * `llamacpp-<model>[-embeddings]-prompt.txt` - Prompt text and token IDs | |
| * `llamacpp-<model>[-embeddings]-tokens.bin` - Binary token IDs for programmatic comparison | |
| These files can be compared against the original model's output to verify the | |
| converted model. | |