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 Desktop
- 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
Download llama.cpp/ggml/src/ggml-virtgpu/ggml-backend.cpp from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 2.67 kB
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/ggml/src/ggml-virtgpu/ggml-backend.cpp
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/ggml/src/ggml-virtgpu/ggml-backend.cpp
-
curl -L -o ggml-backend.cpp https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/ggml/src/ggml-virtgpu/ggml-backend.cpp
2.67 kB
| static const char * ggml_backend_remoting_get_name(ggml_backend_t backend) { | |
| UNUSED(backend); | |
| return "API Remoting backend"; | |
| } | |
| static void ggml_backend_remoting_free(ggml_backend_t backend) { | |
| delete backend; | |
| } | |
| static ggml_status ggml_backend_remoting_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { | |
| virtgpu * gpu = DEV_TO_GPU(backend->device); | |
| return apir_backend_graph_compute(gpu, cgraph); | |
| } | |
| static void ggml_backend_remoting_graph_optimize(ggml_backend_t backend, ggml_cgraph * cgraph) { | |
| virtgpu * gpu = DEV_TO_GPU(backend->device); | |
| UNUSED(gpu); | |
| UNUSED(cgraph); | |
| // not working yet | |
| apir_backend_graph_optimize(gpu, cgraph); | |
| } | |
| static ggml_backend_i ggml_backend_remoting_interface = { | |
| /* .get_name = */ ggml_backend_remoting_get_name, | |
| /* .free = */ ggml_backend_remoting_free, | |
| /* .set_tensor_async = */ NULL, // ggml_backend_remoting_set_tensor_async, | |
| /* .get_tensor_async = */ NULL, // ggml_backend_remoting_get_tensor_async, | |
| /* .set_tensor_2d_async = */ NULL, | |
| /* .get_tensor_2d_async = */ NULL, | |
| /* .cpy_tensor_async = */ NULL, // ggml_backend_remoting_cpy_tensor_async, | |
| /* .synchronize = */ NULL, // ggml_backend_remoting_synchronize, | |
| /* .graph_plan_create = */ NULL, | |
| /* .graph_plan_free = */ NULL, | |
| /* .graph_plan_update = */ NULL, | |
| /* .graph_plan_compute = */ NULL, | |
| /* .graph_compute = */ ggml_backend_remoting_graph_compute, | |
| /* .event_record = */ NULL, | |
| /* .event_wait = */ NULL, | |
| /* .graph_optimize = */ ggml_backend_remoting_graph_optimize, | |
| }; | |
| static ggml_guid_t ggml_backend_remoting_guid() { | |
| static ggml_guid guid = { 0xb8, 0xf7, 0x4f, 0x86, 0x14, 0x03, 0x86, 0x02, | |
| 0x91, 0xc8, 0xdd, 0xe9, 0x02, 0x3f, 0xc0, 0x2b }; | |
| return &guid; | |
| } | |
| ggml_backend_t ggml_backend_remoting_device_init(ggml_backend_dev_t dev, const char * params) { | |
| UNUSED(params); | |
| ggml_backend_remoting_device_context * ctx = (ggml_backend_remoting_device_context *) dev->context; | |
| ggml_backend_t remoting_backend = new ggml_backend{ | |
| /* .guid = */ ggml_backend_remoting_guid(), | |
| /* .interface = */ ggml_backend_remoting_interface, | |
| /* .device = */ ggml_backend_reg_dev_get(ggml_backend_virtgpu_reg(), ctx->device), | |
| /* .context = */ ctx, | |
| }; | |
| return remoting_backend; | |
| } | |