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-buffer.cpp from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 5.56 kB
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/ggml/src/ggml-virtgpu/ggml-backend-buffer.cpp
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/ggml/src/ggml-virtgpu/ggml-backend-buffer.cpp
-
curl -L -o ggml-backend-buffer.cpp https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/ggml/src/ggml-virtgpu/ggml-backend-buffer.cpp
5.56 kB
| static void * ggml_backend_remoting_buffer_get_base(ggml_backend_buffer_t buffer) { | |
| ggml_backend_remoting_buffer_context * context = (ggml_backend_remoting_buffer_context *) buffer->context; | |
| if (context->base) { | |
| return context->base; | |
| } | |
| context->base = apir_buffer_get_base(BUFFER_TO_GPU(buffer), BUFFER_TO_APIR_CONTEXT(buffer)); | |
| return context->base; | |
| } | |
| static void ggml_backend_remoting_buffer_set_tensor(ggml_backend_buffer_t buffer, | |
| ggml_tensor * tensor, | |
| const void * data, | |
| size_t offset, | |
| size_t size) { | |
| virtgpu * gpu = BUFFER_TO_GPU(buffer); | |
| ggml_backend_remoting_buffer_context * context = BUFFER_TO_GGML_CONTEXT(buffer); | |
| if (context->is_from_ptr) { | |
| memcpy((char *) tensor->data + offset, data, size); | |
| } else { | |
| apir_buffer_set_tensor(gpu, BUFFER_TO_APIR_CONTEXT(buffer), tensor, data, offset, size); | |
| } | |
| return; | |
| } | |
| static void ggml_backend_remoting_buffer_get_tensor(ggml_backend_buffer_t buffer, | |
| const ggml_tensor * tensor, | |
| void * data, | |
| size_t offset, | |
| size_t size) { | |
| virtgpu * gpu = BUFFER_TO_GPU(buffer); | |
| ggml_backend_remoting_buffer_context * context = BUFFER_TO_GGML_CONTEXT(buffer); | |
| if (context->is_from_ptr) { | |
| memcpy(data, (const char *) tensor->data + offset, size); | |
| } else { | |
| apir_buffer_get_tensor(gpu, BUFFER_TO_APIR_CONTEXT(buffer), tensor, data, offset, size); | |
| } | |
| } | |
| static void ggml_backend_remoting_buffer_set_tensor_from_ptr(ggml_backend_buffer_t buffer, | |
| ggml_tensor * tensor, | |
| const void * data, | |
| size_t offset, | |
| size_t size) { | |
| UNUSED(buffer); | |
| memcpy((char *) tensor->data + offset, data, size); | |
| return; | |
| } | |
| static void ggml_backend_remoting_buffer_get_tensor_from_ptr(ggml_backend_buffer_t buffer, | |
| const ggml_tensor * tensor, | |
| void * data, | |
| size_t offset, | |
| size_t size) { | |
| UNUSED(buffer); | |
| memcpy(data, (const char *) tensor->data + offset, size); | |
| } | |
| static bool ggml_backend_remoting_buffer_cpy_tensor(ggml_backend_buffer_t buffer, | |
| const ggml_tensor * src, | |
| ggml_tensor * dst) { | |
| virtgpu * gpu = BUFFER_TO_GPU(buffer); | |
| bool ret = apir_buffer_cpy_tensor(gpu, BUFFER_TO_APIR_CONTEXT(buffer), src, dst); | |
| return ret; | |
| } | |
| static void ggml_backend_remoting_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { | |
| virtgpu * gpu = BUFFER_TO_GPU(buffer); | |
| apir_buffer_clear(gpu, BUFFER_TO_APIR_CONTEXT(buffer), value); | |
| return; | |
| } | |
| static void ggml_backend_remoting_buffer_free_buffer(ggml_backend_buffer_t buffer) { | |
| virtgpu * gpu = BUFFER_TO_GPU(buffer); | |
| apir_buffer_free_buffer(gpu, BUFFER_TO_APIR_CONTEXT(buffer)); | |
| ggml_backend_remoting_buffer_context * context = BUFFER_TO_GGML_CONTEXT(buffer); | |
| free(context); | |
| buffer->context = NULL; | |
| } | |
| const ggml_backend_buffer_i ggml_backend_remoting_buffer_interface = { | |
| /* .free_buffer = */ ggml_backend_remoting_buffer_free_buffer, | |
| /* .get_base = */ ggml_backend_remoting_buffer_get_base, | |
| /* .init_tensor = */ NULL, | |
| /* .memset_tensor = */ NULL, | |
| /* .set_tensor = */ ggml_backend_remoting_buffer_set_tensor, | |
| /* .get_tensor = */ ggml_backend_remoting_buffer_get_tensor, | |
| /* .set_tensor_2d = */ NULL, | |
| /* .get_tensor_2d = */ NULL, | |
| /* .cpy_tensor = */ ggml_backend_remoting_buffer_cpy_tensor, | |
| /* .clear = */ ggml_backend_remoting_buffer_clear, | |
| /* .reset = */ NULL, | |
| }; | |
| const ggml_backend_buffer_i ggml_backend_remoting_buffer_from_ptr_interface = { | |
| /* .free_buffer = */ ggml_backend_remoting_buffer_free_buffer, | |
| /* .get_base = */ ggml_backend_remoting_buffer_get_base, | |
| /* .init_tensor = */ NULL, | |
| /* .memset_tensor = */ NULL, | |
| /* .set_tensor = */ ggml_backend_remoting_buffer_set_tensor_from_ptr, | |
| /* .get_tensor = */ ggml_backend_remoting_buffer_get_tensor_from_ptr, | |
| /* .set_tensor_2d = */ NULL, | |
| /* .get_tensor_2d = */ NULL, | |
| /* .cpy_tensor = */ ggml_backend_remoting_buffer_cpy_tensor, | |
| /* .clear = */ ggml_backend_remoting_buffer_clear, | |
| /* .reset = */ NULL, | |
| }; | |