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: 5,558 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 | #include "ggml-remoting.h"
#define BUFFER_TO_GPU(name) ((ggml_backend_remoting_buffer_context *) (name)->context)->gpu
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,
};
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