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/virtgpu-forward-device.cpp from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 5.85 kB
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-forward-device.cpp
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-forward-device.cpp
-
curl -L -o virtgpu-forward-device.cpp https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-forward-device.cpp
5.85 kB
| int apir_device_get_count(virtgpu * gpu) { | |
| apir_encoder * encoder; | |
| apir_decoder * decoder; | |
| ApirForwardReturnCode ret; | |
| REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_COUNT); | |
| REMOTE_CALL(gpu, encoder, decoder, ret); | |
| int32_t dev_count = -1; | |
| apir_decode_int32_t(decoder, &dev_count); | |
| remote_call_finish(gpu, encoder, decoder); | |
| return dev_count; | |
| } | |
| char * apir_device_get_name(virtgpu * gpu) { | |
| apir_encoder * encoder; | |
| apir_decoder * decoder; | |
| ApirForwardReturnCode ret; | |
| REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_NAME); | |
| REMOTE_CALL(gpu, encoder, decoder, ret); | |
| const size_t string_size = apir_decode_array_size_unchecked(decoder); | |
| char * string = (char *) apir_decoder_alloc_array(sizeof(char), string_size); | |
| if (!string) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU "%s: Could not allocate the device name buffer\n", __func__); | |
| return NULL; | |
| } | |
| apir_decode_char_array(decoder, string, string_size); | |
| remote_call_finish(gpu, encoder, decoder); | |
| return string; | |
| } | |
| char * apir_device_get_description(virtgpu * gpu) { | |
| apir_encoder * encoder; | |
| apir_decoder * decoder; | |
| ApirForwardReturnCode ret; | |
| REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_DESCRIPTION); | |
| REMOTE_CALL(gpu, encoder, decoder, ret); | |
| const size_t string_size = apir_decode_array_size_unchecked(decoder); | |
| char * string = (char *) apir_decoder_alloc_array(sizeof(char), string_size); | |
| if (!string) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU "%s: Could not allocate the device description buffer\n", __func__); | |
| return NULL; | |
| } | |
| apir_decode_char_array(decoder, string, string_size); | |
| remote_call_finish(gpu, encoder, decoder); | |
| return string; | |
| } | |
| uint32_t apir_device_get_type(virtgpu * gpu) { | |
| static uint32_t dev_type = 255; | |
| if (dev_type != 255) { | |
| return dev_type; | |
| } | |
| apir_encoder * encoder; | |
| apir_decoder * decoder; | |
| ApirForwardReturnCode ret; | |
| REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_TYPE); | |
| REMOTE_CALL(gpu, encoder, decoder, ret); | |
| apir_decode_uint32_t(decoder, &dev_type); | |
| remote_call_finish(gpu, encoder, decoder); | |
| return dev_type; | |
| } | |
| void apir_device_get_memory(virtgpu * gpu, size_t * free, size_t * total) { | |
| static size_t dev_free = 0; | |
| static size_t dev_total = 0; | |
| apir_encoder * encoder; | |
| apir_decoder * decoder; | |
| ApirForwardReturnCode ret; | |
| REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_MEMORY); | |
| REMOTE_CALL(gpu, encoder, decoder, ret); | |
| apir_decode_size_t(decoder, &dev_free); | |
| apir_decode_size_t(decoder, &dev_total); | |
| *free = dev_free; | |
| *total = dev_total; | |
| remote_call_finish(gpu, encoder, decoder); | |
| return; | |
| } | |
| bool apir_device_supports_op(virtgpu * gpu, const ggml_tensor * op) { | |
| apir_encoder * encoder; | |
| apir_decoder * decoder; | |
| ApirForwardReturnCode ret; | |
| REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_SUPPORTS_OP); | |
| apir_encode_ggml_tensor_inline(encoder, op); | |
| REMOTE_CALL(gpu, encoder, decoder, ret); | |
| bool supports_op; | |
| apir_decode_bool_t(decoder, &supports_op); | |
| remote_call_finish(gpu, encoder, decoder); | |
| return supports_op; | |
| } | |
| apir_buffer_type_host_handle_t apir_device_get_buffer_type(virtgpu * gpu) { | |
| apir_encoder * encoder; | |
| apir_decoder * decoder; | |
| ApirForwardReturnCode ret; | |
| REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_BUFFER_TYPE); | |
| REMOTE_CALL(gpu, encoder, decoder, ret); | |
| apir_buffer_type_host_handle_t buft_handle; | |
| apir_decode_apir_buffer_type_host_handle_t(decoder, &buft_handle); | |
| remote_call_finish(gpu, encoder, decoder); | |
| return buft_handle; | |
| } | |
| void apir_device_get_props(virtgpu * gpu, | |
| bool * async, | |
| bool * host_buffer, | |
| bool * buffer_from_host_ptr, | |
| bool * events) { | |
| apir_encoder * encoder; | |
| apir_decoder * decoder; | |
| ApirForwardReturnCode ret; | |
| REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_PROPS); | |
| REMOTE_CALL(gpu, encoder, decoder, ret); | |
| apir_decode_bool_t(decoder, async); | |
| apir_decode_bool_t(decoder, host_buffer); | |
| apir_decode_bool_t(decoder, buffer_from_host_ptr); | |
| apir_decode_bool_t(decoder, events); | |
| remote_call_finish(gpu, encoder, decoder); | |
| return; | |
| } | |
| apir_buffer_context_t apir_device_buffer_from_ptr(virtgpu * gpu, size_t size, size_t max_tensor_size) { | |
| apir_encoder * encoder; | |
| apir_decoder * decoder; | |
| ApirForwardReturnCode ret; | |
| apir_buffer_context_t buffer_context; | |
| REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_BUFFER_FROM_PTR); | |
| if (virtgpu_shmem_create(gpu, size, &buffer_context.shmem)) { | |
| GGML_ABORT(GGML_VIRTGPU "%s: Couldn't allocate %ldb of guest-host shared buffer", __func__, size); | |
| } | |
| apir_encode_virtgpu_shmem_res_id(encoder, buffer_context.shmem.res_id); | |
| apir_encode_size_t(encoder, &size); | |
| apir_encode_size_t(encoder, &max_tensor_size); | |
| REMOTE_CALL(gpu, encoder, decoder, ret); | |
| apir_decode_apir_buffer_host_handle_t(decoder, &buffer_context.host_handle); | |
| buffer_context.buft_host_handle = apir_decode_apir_buffer_type_host_handle(decoder); | |
| remote_call_finish(gpu, encoder, decoder); | |
| return buffer_context; | |
| } | |