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: 2,672 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 | #include "../../include/ggml-virtgpu.h"
#include "ggml-remoting.h"
static const char * ggml_backend_remoting_get_name(ggml_backend_t backend) {
UNUSED(backend);
return "API Remoting backend";
}
static void ggml_backend_remoting_free(ggml_backend_t backend) {
delete backend;
}
static ggml_status ggml_backend_remoting_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
virtgpu * gpu = DEV_TO_GPU(backend->device);
return apir_backend_graph_compute(gpu, cgraph);
}
static void ggml_backend_remoting_graph_optimize(ggml_backend_t backend, ggml_cgraph * cgraph) {
virtgpu * gpu = DEV_TO_GPU(backend->device);
#if true
UNUSED(gpu);
UNUSED(cgraph);
#else
// not working yet
apir_backend_graph_optimize(gpu, cgraph);
#endif
}
static ggml_backend_i ggml_backend_remoting_interface = {
/* .get_name = */ ggml_backend_remoting_get_name,
/* .free = */ ggml_backend_remoting_free,
/* .set_tensor_async = */ NULL, // ggml_backend_remoting_set_tensor_async,
/* .get_tensor_async = */ NULL, // ggml_backend_remoting_get_tensor_async,
/* .set_tensor_2d_async = */ NULL,
/* .get_tensor_2d_async = */ NULL,
/* .cpy_tensor_async = */ NULL, // ggml_backend_remoting_cpy_tensor_async,
/* .synchronize = */ NULL, // ggml_backend_remoting_synchronize,
/* .graph_plan_create = */ NULL,
/* .graph_plan_free = */ NULL,
/* .graph_plan_update = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_remoting_graph_compute,
/* .event_record = */ NULL,
/* .event_wait = */ NULL,
/* .graph_optimize = */ ggml_backend_remoting_graph_optimize,
};
static ggml_guid_t ggml_backend_remoting_guid() {
static ggml_guid guid = { 0xb8, 0xf7, 0x4f, 0x86, 0x14, 0x03, 0x86, 0x02,
0x91, 0xc8, 0xdd, 0xe9, 0x02, 0x3f, 0xc0, 0x2b };
return &guid;
}
ggml_backend_t ggml_backend_remoting_device_init(ggml_backend_dev_t dev, const char * params) {
UNUSED(params);
ggml_backend_remoting_device_context * ctx = (ggml_backend_remoting_device_context *) dev->context;
ggml_backend_t remoting_backend = new ggml_backend{
/* .guid = */ ggml_backend_remoting_guid(),
/* .interface = */ ggml_backend_remoting_interface,
/* .device = */ ggml_backend_reg_dev_get(ggml_backend_virtgpu_reg(), ctx->device),
/* .context = */ ctx,
};
return remoting_backend;
}
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