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 Studio
How to use OmnipotentFool/Aurvion with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for OmnipotentFool/Aurvion to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for OmnipotentFool/Aurvion to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for OmnipotentFool/Aurvion to start chatting
- 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
| void test_json_parser(testing &t) { | |
| // Test parsing a simple JSON object | |
| t.test("simple JSON object parsing", [](testing &t) { | |
| auto json = build_peg_parser([](common_peg_parser_builder & p) { return p.json(); }); | |
| std::string input = R"({"name": "test", "value": 42, "flag": true})"; | |
| common_peg_parse_context ctx(input); | |
| auto result = json.parse(ctx); | |
| t.assert_equal("result_is_success", true, result.success()); | |
| t.assert_equal("result_end", input.size(), result.end); | |
| }); | |
| // Test parsing a JSON array with mixed types | |
| t.test("JSON array with mixed types", [](testing &t) { | |
| auto json = build_peg_parser([](common_peg_parser_builder & p) { return p.json(); }); | |
| std::string input = R"([1, "hello", true, null, 3.14])"; | |
| common_peg_parse_context ctx(input); | |
| auto result = json.parse(ctx); | |
| t.assert_equal("result_is_success", true, result.success()); | |
| t.assert_equal("result_end", input.size(), result.end); | |
| }); | |
| // Test parsing nested JSON with objects and arrays | |
| t.test("nested JSON with objects and arrays", [](testing &t) { | |
| auto json = build_peg_parser([](common_peg_parser_builder & p) { return p.json(); }); | |
| std::string input = | |
| R"({"users": [{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}], "count": 2, "metadata": {"version": "1.0", "tags": ["admin", "user"]}})"; | |
| common_peg_parse_context ctx(input); | |
| auto result = json.parse(ctx); | |
| t.assert_equal("result_is_success", true, result.success()); | |
| t.assert_equal("result_end", input.size(), result.end); | |
| }); | |
| // Test need_more_input() parsing - incomplete object | |
| t.test("need_more_input() parsing - incomplete object", [](testing &t) { | |
| auto json = build_peg_parser([](common_peg_parser_builder & p) { return p.json(); }); | |
| std::string input = R"({"name": "test", "value": )"; | |
| common_peg_parse_context ctx(input, COMMON_PEG_PARSE_FLAG_LENIENT); | |
| auto result = json.parse(ctx); | |
| t.assert_equal("result_is_need_more_input", true, result.need_more_input()); | |
| }); | |
| // Test need_more_input() parsing - incomplete array | |
| t.test("need_more_input() parsing - incomplete array", [](testing &t) { | |
| auto json = build_peg_parser([](common_peg_parser_builder & p) { return p.json(); }); | |
| std::string input = R"([1, 2, 3, )"; | |
| common_peg_parse_context ctx(input, COMMON_PEG_PARSE_FLAG_LENIENT); | |
| auto result = json.parse(ctx); | |
| t.assert_equal("result_is_need_more_input", true, result.need_more_input()); | |
| }); | |
| // Test need_more_input() parsing - incomplete nested structure | |
| t.test("need_more_input() parsing - incomplete nested structure", [](testing &t) { | |
| auto json = build_peg_parser([](common_peg_parser_builder & p) { return p.json(); }); | |
| std::string input = R"({"data": {"nested": )"; | |
| common_peg_parse_context ctx(input, COMMON_PEG_PARSE_FLAG_LENIENT); | |
| auto result = json.parse(ctx); | |
| t.assert_equal("result_is_need_more_input", true, result.need_more_input()); | |
| }); | |
| t.test("object member", [](testing &t) { | |
| auto parser = build_peg_parser([](common_peg_parser_builder & p) { | |
| return p.json_member("name", "\"" + p.chars("[a-z]") + "\""); | |
| }); | |
| t.test("success", [&](testing &t) { | |
| std::string input = R"("name": "bob")"; | |
| common_peg_parse_context ctx(input); | |
| auto result = parser.parse(ctx); | |
| t.assert_true("success", result.success()); | |
| }); | |
| t.test("partial", [&](testing &t) { | |
| std::string input = R"("name": "bo)"; | |
| common_peg_parse_context ctx(input, COMMON_PEG_PARSE_FLAG_LENIENT); | |
| auto result = parser.parse(ctx); | |
| t.assert_true("need more input", result.need_more_input()); | |
| }); | |
| t.test("failed", [&](testing &t) { | |
| std::string input = R"([])"; | |
| common_peg_parse_context ctx(input); | |
| auto result = parser.parse(ctx); | |
| t.assert_true("fail", result.fail()); | |
| }); | |
| }); | |
| } | |