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/src/llama-impl.h from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 2.73 kB
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/src/llama-impl.h
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/src/llama-impl.h
-
curl -L -o llama-impl.h https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/src/llama-impl.h
2.73 kB
| // | |
| // logging | |
| // | |
| LLAMA_ATTRIBUTE_FORMAT(2, 3) | |
| void llama_log_internal (ggml_log_level level, const char * format, ...); | |
| void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data); | |
| // | |
| // helpers | |
| // | |
| template <typename T> | |
| struct no_init { | |
| T value; | |
| no_init() = default; | |
| }; | |
| template <typename dst_t, typename src_t> | |
| static inline dst_t llama_cast(src_t v) { | |
| if constexpr (std::is_same_v<src_t, dst_t>) { | |
| return v; | |
| } else if constexpr (std::is_same_v<src_t, ggml_fp16_t> && std::is_same_v<dst_t, float>) { | |
| return ggml_fp16_to_fp32(v); | |
| } else if constexpr (std::is_same_v<src_t, float> && std::is_same_v<dst_t, ggml_fp16_t>) { | |
| return ggml_fp32_to_fp16(v); | |
| } else { | |
| static_assert(std::is_same_v<dst_t, void>, "unsupported type combination"); | |
| } | |
| } | |
| struct time_meas { | |
| time_meas(int64_t & t_acc, bool disable = false); | |
| ~time_meas(); | |
| const int64_t t_start_us; | |
| int64_t & t_acc; | |
| }; | |
| template <typename T> | |
| struct buffer_view { | |
| T * data; | |
| size_t size = 0; | |
| bool has_data() const { | |
| return data && size > 0; | |
| } | |
| }; | |
| void replace_all(std::string & s, const std::string & search, const std::string & replace); | |
| // TODO: rename to llama_format ? | |
| LLAMA_ATTRIBUTE_FORMAT(1, 2) | |
| std::string format(const char * fmt, ...); | |
| std::string llama_format_tensor_shape(const std::vector<int64_t> & ne); | |
| std::string llama_format_tensor_shape(const struct ggml_tensor * t); | |
| std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i); | |