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-shm.cpp from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 2.84 kB
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-shm.cpp
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-shm.cpp
-
curl -L -o virtgpu-shm.cpp https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-shm.cpp
2.84 kB
| static uint32_t virtgpu_ioctl_resource_create_blob(virtgpu * gpu, | |
| uint32_t blob_mem, | |
| uint32_t blob_flags, | |
| size_t blob_size, | |
| uint64_t blob_id, | |
| uint32_t * res_id) { | |
| blob_size = align64(blob_size, 4096); | |
| drm_virtgpu_resource_create_blob args = { | |
| .blob_mem = blob_mem, | |
| .blob_flags = blob_flags, | |
| .bo_handle = 0, | |
| .res_handle = 0, | |
| .size = blob_size, | |
| .pad = 0, | |
| .cmd_size = 0, | |
| .cmd = 0, | |
| .blob_id = blob_id, | |
| }; | |
| if (virtgpu_ioctl(gpu, DRM_IOCTL_VIRTGPU_RESOURCE_CREATE_BLOB, &args)) { | |
| return 0; | |
| } | |
| *res_id = args.res_handle; | |
| return args.bo_handle; | |
| } | |
| static void virtgpu_ioctl_gem_close(virtgpu * gpu, uint32_t gem_handle) { | |
| drm_gem_close args = { | |
| .handle = gem_handle, | |
| .pad = 0, | |
| }; | |
| const int ret = virtgpu_ioctl(gpu, DRM_IOCTL_GEM_CLOSE, &args); | |
| assert(!ret); | |
| UNUSED(ret); | |
| } | |
| static void * virtgpu_ioctl_map(virtgpu * gpu, uint32_t gem_handle, size_t size) { | |
| drm_virtgpu_map args = { | |
| .offset = 0, | |
| .handle = gem_handle, | |
| .pad = 0, | |
| }; | |
| if (virtgpu_ioctl(gpu, DRM_IOCTL_VIRTGPU_MAP, &args)) { | |
| return NULL; | |
| } | |
| void * ptr = mmap(NULL, size, PROT_READ | PROT_WRITE, MAP_SHARED, gpu->fd, args.offset); | |
| if (ptr == MAP_FAILED) { | |
| return NULL; | |
| } | |
| return ptr; | |
| } | |
| void virtgpu_shmem_destroy(virtgpu * gpu, virtgpu_shmem * shmem) { | |
| munmap(shmem->mmap_ptr, shmem->mmap_size); | |
| virtgpu_ioctl_gem_close(gpu, shmem->gem_handle); | |
| } | |
| int virtgpu_shmem_create(virtgpu * gpu, size_t size, virtgpu_shmem * shmem) { | |
| size = align64(size, 16384); | |
| uint32_t res_id; | |
| uint32_t gem_handle = virtgpu_ioctl_resource_create_blob(gpu, VIRTGPU_BLOB_MEM_HOST3D, | |
| VIRTGPU_BLOB_FLAG_USE_MAPPABLE, size, 0, &res_id); | |
| if (!gem_handle) { | |
| return 1; | |
| } | |
| void * ptr = virtgpu_ioctl_map(gpu, gem_handle, size); | |
| if (!ptr) { | |
| virtgpu_ioctl_gem_close(gpu, gem_handle); | |
| GGML_LOG_ERROR(GGML_VIRTGPU "%s: virtgpu_ioctl_map failed\n", __func__); | |
| return 1; | |
| } | |
| shmem->res_id = res_id; | |
| shmem->mmap_size = size; | |
| shmem->mmap_ptr = ptr; | |
| shmem->gem_handle = gem_handle; | |
| return 0; | |
| } | |