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/common/jinja/string.h from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 1.82 kB
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/common/jinja/string.h
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/common/jinja/string.h
-
curl -L -o string.h https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/common/jinja/string.h
1.82 kB
| namespace jinja { | |
| // allow differentiate between user input strings and template strings | |
| // transformations should handle this information as follows: | |
| // - one-to-one (e.g., uppercase, lowercase): preserve is_input flag | |
| // - one-to-many (e.g., strip): if input string is marked as is_input, all resulting parts should be marked as is_input | |
| // - many-to-one (e.g., concat): if ALL input parts are marked as is_input, resulting part should be marked as is_input | |
| struct string_part { | |
| bool is_input = false; // may skip parsing special tokens if true | |
| std::string val; | |
| bool is_uppercase() const; | |
| bool is_lowercase() const; | |
| }; | |
| struct string { | |
| std::vector<string_part> parts; | |
| string() = default; | |
| string(const std::string & v, bool user_input = false) { | |
| parts.push_back({user_input, v}); | |
| } | |
| string(int v) { | |
| parts.push_back({false, std::to_string(v)}); | |
| } | |
| string(double v) { | |
| parts.push_back({false, std::to_string(v)}); | |
| } | |
| // mark all parts as user input | |
| void mark_input(); | |
| std::string str() const; | |
| size_t length() const; | |
| void hash_update(hasher & hash) const noexcept; | |
| bool all_parts_are_input() const; | |
| bool is_uppercase() const; | |
| bool is_lowercase() const; | |
| // mark this string as input if other has ALL parts as input | |
| void mark_input_based_on(const string & other); | |
| string append(const string & other); | |
| // in-place transformations | |
| string uppercase(); | |
| string lowercase(); | |
| string capitalize(); | |
| string titlecase(); | |
| string strip(bool left, bool right, std::optional<const std::string_view> chars = std::nullopt); | |
| }; | |
| } // namespace jinja | |