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
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -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
- Llama new
- 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/backend/shared/api_remoting.h from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 3.41 kB
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/ggml/src/ggml-virtgpu/backend/shared/api_remoting.h
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/ggml/src/ggml-virtgpu/backend/shared/api_remoting.h
-
curl -L -o api_remoting.h https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/ggml/src/ggml-virtgpu/backend/shared/api_remoting.h
3.41 kB
| /* the rest of this file must match virglrenderer/src/apir-protocol.h */ | |
| enum ApirCommandType { | |
| APIR_COMMAND_TYPE_HANDSHAKE = 0, | |
| APIR_COMMAND_TYPE_LOADLIBRARY = 1, | |
| APIR_COMMAND_TYPE_FORWARD = 2, | |
| APIR_COMMAND_TYPE_LENGTH = 3, | |
| }; | |
| typedef uint64_t ApirCommandFlags; | |
| enum ApirLoadLibraryReturnCode { | |
| APIR_LOAD_LIBRARY_SUCCESS = 0, | |
| // these error codes are returned by the Virglrenderer APIR component | |
| APIR_LOAD_LIBRARY_HYPERCALL_INITIALIZATION_ERROR = 1, | |
| APIR_LOAD_LIBRARY_ALREADY_LOADED = 2, | |
| APIR_LOAD_LIBRARY_ENV_VAR_MISSING = 3, | |
| APIR_LOAD_LIBRARY_CANNOT_OPEN = 4, | |
| APIR_LOAD_LIBRARY_SYMBOL_MISSING = 5, | |
| // any value greater than this is an APIR *backend library* initialization return code | |
| APIR_LOAD_LIBRARY_INIT_BASE_INDEX = 6, | |
| }; | |
| enum ApirForwardReturnCode { | |
| APIR_FORWARD_SUCCESS = 0, | |
| // these error codes are returned by the Virglrenderer APIR component | |
| APIR_FORWARD_NO_DISPATCH_FCT = 1, | |
| APIR_FORWARD_TIMEOUT = 2, | |
| APIR_FORWARD_FAILED_TO_SYNC_STREAMS = 3, | |
| // any value greater than this index an APIR *backend library* forward return code | |
| APIR_FORWARD_BASE_INDEX = 4, | |
| }; | |
| __attribute__((unused)) static inline const char * apir_command_name(ApirCommandType type) { | |
| switch (type) { | |
| case APIR_COMMAND_TYPE_HANDSHAKE: | |
| return "HandShake"; | |
| case APIR_COMMAND_TYPE_LOADLIBRARY: | |
| return "LoadLibrary"; | |
| case APIR_COMMAND_TYPE_FORWARD: | |
| return "Forward"; | |
| default: | |
| return "unknown"; | |
| } | |
| } | |
| __attribute__((unused)) static const char * apir_load_library_error(ApirLoadLibraryReturnCode code) { | |
| do { \ | |
| if (code == code_name) \ | |
| return #code_name; \ | |
| } while (0) | |
| APIR_LOAD_LIBRARY_ERROR(APIR_LOAD_LIBRARY_SUCCESS); | |
| APIR_LOAD_LIBRARY_ERROR(APIR_LOAD_LIBRARY_HYPERCALL_INITIALIZATION_ERROR); | |
| APIR_LOAD_LIBRARY_ERROR(APIR_LOAD_LIBRARY_ALREADY_LOADED); | |
| APIR_LOAD_LIBRARY_ERROR(APIR_LOAD_LIBRARY_ENV_VAR_MISSING); | |
| APIR_LOAD_LIBRARY_ERROR(APIR_LOAD_LIBRARY_CANNOT_OPEN); | |
| APIR_LOAD_LIBRARY_ERROR(APIR_LOAD_LIBRARY_SYMBOL_MISSING); | |
| APIR_LOAD_LIBRARY_ERROR(APIR_LOAD_LIBRARY_INIT_BASE_INDEX); | |
| return "Unknown APIR_COMMAND_TYPE_LoadLibrary error"; | |
| } | |
| __attribute__((unused)) static const char * apir_forward_error(ApirForwardReturnCode code) { | |
| do { \ | |
| if (code == code_name) \ | |
| return #code_name; \ | |
| } while (0) | |
| APIR_FORWARD_ERROR(APIR_FORWARD_SUCCESS); | |
| APIR_FORWARD_ERROR(APIR_FORWARD_NO_DISPATCH_FCT); | |
| APIR_FORWARD_ERROR(APIR_FORWARD_TIMEOUT); | |
| APIR_FORWARD_ERROR(APIR_FORWARD_FAILED_TO_SYNC_STREAMS); | |
| APIR_FORWARD_ERROR(APIR_FORWARD_BASE_INDEX); | |
| return "Unknown APIR_COMMAND_TYPE_FORWARD error"; | |
| } | |