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
File size: 6,266 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 | #include "ggml-remoting.h"
#include <mutex>
static const char * ggml_backend_remoting_device_get_name(ggml_backend_dev_t dev) {
virtgpu * gpu = DEV_TO_GPU(dev);
// Return the prefixed name that was built once during initialization
return gpu->cached_device_info.name;
}
static const char * ggml_backend_remoting_device_get_description(ggml_backend_dev_t dev) {
virtgpu * gpu = DEV_TO_GPU(dev);
// Return the pre-cached description from the virtgpu structure
return gpu->cached_device_info.description;
}
static enum ggml_backend_dev_type ggml_backend_remoting_device_get_type(ggml_backend_dev_t dev) {
virtgpu * gpu = DEV_TO_GPU(dev);
return (enum ggml_backend_dev_type) gpu->cached_device_info.type;
}
static void ggml_backend_remoting_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
virtgpu * gpu = DEV_TO_GPU(dev);
*free = gpu->cached_device_info.memory_free;
*total = gpu->cached_device_info.memory_total;
}
static bool ggml_backend_remoting_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
#if USE_ALWAYS_TRUE_SUPPORTS_OP == 1
/* ggml-rpc cheats it like this */
/* with the current implementation of serialize_tensor, the src/view aren't properly passed */
UNUSED(dev);
UNUSED(op);
return true;
#else
virtgpu * gpu = DEV_TO_GPU(dev);
return apir_device_supports_op(gpu, op);
#endif
}
static bool ggml_backend_remoting_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
bool supported = buft->device == dev;
return supported;
}
static bool ggml_backend_remoting_device_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
UNUSED(dev);
UNUSED(op);
return false;
}
static void ggml_backend_remoting_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) {
props->name = ggml_backend_remoting_device_get_name(dev);
props->description = ggml_backend_remoting_device_get_description(dev);
props->type = ggml_backend_remoting_device_get_type(dev);
ggml_backend_remoting_device_get_memory(dev, &props->memory_free, &props->memory_total);
virtgpu * gpu = DEV_TO_GPU(dev);
apir_device_get_props(gpu, &props->caps.async, &props->caps.host_buffer, &props->caps.buffer_from_host_ptr,
&props->caps.events);
props->caps.buffer_from_host_ptr = false;
props->caps.async = false;
props->caps.events = false;
}
ggml_backend_buffer_type_t ggml_backend_remoting_device_get_buffer_type(ggml_backend_dev_t dev) {
virtgpu * gpu = DEV_TO_GPU(dev);
static std::atomic<bool> initialized = false;
static ggml_backend_buffer_type buft;
if (!initialized) {
static std::mutex mutex;
std::lock_guard<std::mutex> lock(mutex);
if (!initialized) {
buft = {
/* .iface = */ ggml_backend_remoting_buffer_type_interface,
/* .device = */ dev,
/* .context = */ (void *) gpu->cached_buffer_type.host_handle,
};
initialized = true;
}
}
return &buft;
}
static ggml_backend_buffer_type_t ggml_backend_remoting_device_get_buffer_from_ptr_type(ggml_backend_dev_t dev) {
virtgpu * gpu = DEV_TO_GPU(dev);
static std::atomic<bool> initialized = false;
static ggml_backend_buffer_type buft;
if (!initialized) {
static std::mutex mutex;
std::lock_guard<std::mutex> lock(mutex);
if (!initialized) {
buft = {
/* .iface = */ ggml_backend_remoting_buffer_from_ptr_type_interface,
/* .device = */ dev,
/* .context = */ (void *) gpu->cached_buffer_type.host_handle,
};
initialized = true;
}
}
return &buft;
}
static ggml_backend_buffer_t ggml_backend_remoting_device_buffer_from_ptr(ggml_backend_dev_t dev,
void * ptr,
size_t size,
size_t max_tensor_size) {
virtgpu * gpu = DEV_TO_GPU(dev);
ggml_backend_remoting_buffer_context * context = (ggml_backend_remoting_buffer_context *) malloc(sizeof(*context));
if (!context) {
GGML_ABORT(GGML_VIRTGPU "%s: Couldn't allocate the buffer context ...", __func__);
}
context->gpu = gpu;
context->apir_context = apir_device_buffer_from_ptr(gpu, size, max_tensor_size);
context->base = ptr;
context->is_from_ptr = true;
ggml_backend_buffer_t buffer =
ggml_backend_buffer_init(ggml_backend_remoting_device_get_buffer_from_ptr_type(dev),
ggml_backend_remoting_buffer_from_ptr_interface, (void *) context, size);
return buffer;
}
const ggml_backend_device_i ggml_backend_remoting_device_interface = {
/* .get_name = */ ggml_backend_remoting_device_get_name,
/* .get_description = */ ggml_backend_remoting_device_get_description,
/* .get_memory = */ ggml_backend_remoting_device_get_memory,
/* .get_type = */ ggml_backend_remoting_device_get_type,
/* .get_props = */ ggml_backend_remoting_device_get_props,
/* .init_backend = */ ggml_backend_remoting_device_init,
/* .get_buffer_type = */ ggml_backend_remoting_device_get_buffer_type,
/* .get_host_buffer_type = */ NULL,
/* .buffer_from_host_ptr = */ ggml_backend_remoting_device_buffer_from_ptr,
/* .supports_op = */ ggml_backend_remoting_device_supports_op,
/* .supports_buft = */ ggml_backend_remoting_device_supports_buft,
/* .offload_op = */ ggml_backend_remoting_device_offload_op,
/* .event_new = */ NULL,
/* .event_free = */ NULL,
/* .event_synchronize = */ NULL,
};
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