Safetensors
GGUF
Turkish
llama
Llama-3
instruct
finetune
chatml
gpt4
synthetic data
distillation
function calling
json mode
axolotl
roleplaying
chat
Instructions to use tda45/TdAI 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 tda45/TdAI 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 tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
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 tda45/TdAI # Run inference directly in the terminal: ./llama-cli -hf tda45/TdAI
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 tda45/TdAI # Run inference directly in the terminal: ./build/bin/llama-cli -hf tda45/TdAI
Use Docker
docker model run hf.co/tda45/TdAI
- LM Studio
- Jan
- Ollama
How to use tda45/TdAI with Ollama:
ollama run hf.co/tda45/TdAI
- Unsloth Desktop
- Docker Model Runner
How to use tda45/TdAI with Docker Model Runner:
docker model run hf.co/tda45/TdAI
- Lemonade
How to use tda45/TdAI with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tda45/TdAI
Run and chat with the model
lemonade run user.TdAI-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Download llama.cpp/tests/peg-parser/test-json-serialization.cpp from tda45/TdAI: direct link, hf CLI and curl.
- Browser
- Download file 1.02 kB
-
https://huggingface.co/tda45/TdAI/resolve/main/llama.cpp/tests/peg-parser/test-json-serialization.cpp
- Command line
-
hf download hf://tda45/TdAI/llama.cpp/tests/peg-parser/test-json-serialization.cpp
-
curl -L -o test-json-serialization.cpp https://huggingface.co/tda45/TdAI/resolve/main/llama.cpp/tests/peg-parser/test-json-serialization.cpp
1.02 kB
| void test_json_serialization(testing &t) { | |
| auto original = build_peg_parser([](common_peg_parser_builder & p) { | |
| return "<tool_call>" + p.json() + "</tool_call>"; | |
| }); | |
| auto json_serialized = original.to_json().dump(); | |
| t.test("compare before/after", [&](testing &t) { | |
| auto deserialized = common_peg_arena::from_json(nlohmann::json::parse(json_serialized)); | |
| // Test complex JSON | |
| std::string input = R"({"name": "test", "values": [1, 2, 3], "nested": {"a": true}})"; | |
| common_peg_parse_context ctx1(input); | |
| common_peg_parse_context ctx2(input); | |
| auto result1 = original.parse(ctx1); | |
| auto result2 = deserialized.parse(ctx2); | |
| t.assert_equal("both_succeed", result1.success(), result2.success()); | |
| t.assert_equal("same_end_pos", result1.end, result2.end); | |
| }); | |
| t.bench("deserialize", [&]() { | |
| auto deserialized = common_peg_arena::from_json(nlohmann::json::parse(json_serialized)); | |
| }, 100); | |
| } | |