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
Download llama.cpp/common/log.h from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 5.71 kB
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/common/log.h
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/common/log.h
-
curl -L -o log.h https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/common/log.h
5.71 kB
| enum log_colors { | |
| LOG_COLORS_AUTO = -1, | |
| LOG_COLORS_DISABLED = 0, | |
| LOG_COLORS_ENABLED = 1, | |
| }; | |
| // needed by the LOG_TMPL macro to avoid computing log arguments if the verbosity lower | |
| // set via common_log_set_verbosity() | |
| int common_log_get_verbosity_thold(void); | |
| void common_log_set_verbosity_thold(int verbosity); // not thread-safe | |
| void common_log_default_callback(enum ggml_log_level level, const char * text, void * user_data); | |
| // the common_log uses an internal worker thread to print/write log messages | |
| // when the worker thread is paused, incoming log messages are discarded | |
| struct common_log; | |
| struct common_log * common_log_init(); | |
| // Singleton, intentionally leaked to avoid Windows teardown hangs. | |
| // Call common_log_flush() before exit if you want to ensure all logs are flushed. | |
| struct common_log * common_log_main(); | |
| void common_log_pause (struct common_log * log); // pause the worker thread, not thread-safe | |
| void common_log_resume(struct common_log * log); // resume the worker thread, not thread-safe | |
| void common_log_free (struct common_log * log); | |
| LOG_ATTRIBUTE_FORMAT(3, 4) | |
| void common_log_add(struct common_log * log, enum ggml_log_level level, const char * fmt, ...); | |
| // defaults: file = NULL, colors = false, prefix = false, timestamps = false | |
| // | |
| // regular log output: | |
| // | |
| // ggml_backend_metal_log_allocated_size: allocated buffer, size = 6695.84 MiB, ( 6695.91 / 21845.34) | |
| // llm_load_tensors: ggml ctx size = 0.27 MiB | |
| // llm_load_tensors: offloading 32 repeating layers to GPU | |
| // llm_load_tensors: offloading non-repeating layers to GPU | |
| // | |
| // with prefix = true, timestamps = true, the log output will look like this: | |
| // | |
| // 0.00.035.060 D ggml_backend_metal_log_allocated_size: allocated buffer, size = 6695.84 MiB, ( 6695.91 / 21845.34) | |
| // 0.00.035.064 I llm_load_tensors: ggml ctx size = 0.27 MiB | |
| // 0.00.090.578 I llm_load_tensors: offloading 32 repeating layers to GPU | |
| // 0.00.090.579 I llm_load_tensors: offloading non-repeating layers to GPU | |
| // | |
| // D - debug (stderr, V = LOG_DEFAULT_DEBUG) | |
| // I - info (stdout, V = LOG_DEFAULT_INFO) | |
| // W - warning (stderr, V = LOG_DEFAULT_WARN) | |
| // E - error (stderr, V = LOG_DEFAULT_ERROR) | |
| // O - output (stdout, V = LOG_DEFAULT_OUTPUT) | |
| // | |
| void common_log_set_file (struct common_log * log, const char * file); // not thread-safe | |
| void common_log_set_colors (struct common_log * log, log_colors colors); // not thread-safe | |
| void common_log_set_prefix (struct common_log * log, bool prefix); // whether to output prefix to each log | |
| void common_log_set_timestamps(struct common_log * log, bool timestamps); // whether to output timestamps in the prefix | |
| void common_log_flush (struct common_log * log); // flush all pending log messages | |
| // helper macros for logging | |
| // use these to avoid computing log arguments if the verbosity of the log is higher than the threshold | |
| // | |
| // for example: | |
| // | |
| // LOG_DBG("this is a debug message: %d\n", expensive_function()); | |
| // | |
| // this will avoid calling expensive_function() if LOG_DEFAULT_DEBUG > common_log_verbosity_thold | |
| // | |
| do { \ | |
| if ((verbosity) <= common_log_get_verbosity_thold()) { \ | |
| common_log_add(common_log_main(), (level), __VA_ARGS__); \ | |
| } \ | |
| } while (0) | |