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-forward-backend.cpp from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 2.06 kB
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-forward-backend.cpp
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-forward-backend.cpp
-
curl -L -o virtgpu-forward-backend.cpp https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-forward-backend.cpp
2.06 kB
| static long long current_time_ms() { | |
| timespec ts; | |
| clock_gettime(CLOCK_REALTIME, &ts); // Use CLOCK_MONOTONIC for elapsed time | |
| return (long long) ts.tv_sec * 1000000000LL + ts.tv_nsec; | |
| } | |
| ggml_status apir_backend_graph_compute(virtgpu * gpu, ggml_cgraph * cgraph) { | |
| apir_encoder * encoder; | |
| apir_decoder * decoder; | |
| ApirForwardReturnCode ret; | |
| REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BACKEND_GRAPH_COMPUTE); | |
| std::vector<uint8_t> cgraph_data; | |
| size_t cgraph_size = apir_serialize_ggml_cgraph(cgraph, cgraph_data); | |
| virtgpu_shmem temp_shmem; // Local storage for large buffers | |
| virtgpu_shmem * shmem = &temp_shmem; | |
| bool using_shared_shmem = false; | |
| if (cgraph_size <= gpu->data_shmem.mmap_size) { | |
| // Lock mutex before using shared data_shmem buffer | |
| if (mtx_lock(&gpu->data_shmem_mutex) != thrd_success) { | |
| GGML_ABORT(GGML_VIRTGPU "%s: Failed to lock data_shmem mutex", __func__); | |
| } | |
| using_shared_shmem = true; | |
| shmem = &gpu->data_shmem; | |
| } else if (virtgpu_shmem_create(gpu, cgraph_size, shmem)) { | |
| GGML_ABORT(GGML_VIRTGPU "%s: Couldn't allocate the guest-host shared buffer", __func__); | |
| } | |
| apir_encode_virtgpu_shmem_res_id(encoder, shmem->res_id); | |
| apir_encode_size_t(encoder, &cgraph_size); | |
| char * shmem_data = (char *) shmem->mmap_ptr; | |
| apir_encoder secondary_enc = apir_new_encoder(shmem_data, cgraph_size); | |
| apir_encode_cgraph_data(&secondary_enc, cgraph_data); | |
| REMOTE_CALL(gpu, encoder, decoder, ret); | |
| ggml_status status = GGML_STATUS_ABORTED; | |
| apir_decode_ggml_status(decoder, &status); | |
| remote_call_finish(gpu, encoder, decoder); | |
| // Unlock mutex before cleanup | |
| if (using_shared_shmem) { | |
| mtx_unlock(&gpu->data_shmem_mutex); | |
| } else { | |
| virtgpu_shmem_destroy(gpu, shmem); | |
| } | |
| return status; | |
| } | |