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
File size: 2,840 Bytes
db65755 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | #include "virtgpu-shm.h"
#include "virtgpu.h"
#include "ggml-remoting.h"
#include <assert.h>
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) {
#ifdef SIMULATE_BO_SIZE_FIX
blob_size = align64(blob_size, 4096);
#endif
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);
#ifdef NDEBUG
UNUSED(ret);
#endif
}
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;
}
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