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.
- Libraries
- llama-cpp-python
How to use tda45/TdAI with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tda45/TdAI", filename="llama.cpp/models/ggml-vocab-aquila.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- 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 Studio
How to use tda45/TdAI 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 tda45/TdAI 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 tda45/TdAI to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tda45/TdAI to start chatting
- Atomic Chat new
- 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
| static void print_usage(int /*argc*/, char ** argv) { | |
| printf( | |
| "\nexamples:\n" | |
| " %s -hf ggml-org/gemma-3-4b-it-qat-GGUF\n" | |
| " %s -hf ggml-org/gemma-3-4b-it-qat-GGUF:Q4_K_M\n" | |
| " %s -hf ggml-org/models -hff model.gguf\n" | |
| " %s -mu https://example.com/model.gguf -m model.gguf\n" | |
| "\n", | |
| argv[0], argv[0], argv[0], argv[0] | |
| ); | |
| } | |
| int llama_download(int argc, char ** argv); | |
| int llama_download(int argc, char ** argv) { | |
| common_init(); | |
| common_params params; | |
| params.verbosity = LOG_LEVEL_ERROR; | |
| if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_DOWNLOAD, print_usage)) { | |
| return 1; | |
| } | |
| const bool has_source = !params.model.hf_repo.empty() || !params.model.url.empty() || | |
| !params.model.path.empty() || !params.model.docker_repo.empty(); | |
| if (!has_source) { | |
| fprintf(stderr, "error: no model source specified (use --hf-repo, --model-url, --model or --docker-repo)\n"); | |
| return 1; | |
| } | |
| try { | |
| common_models_handler handler = common_models_handler_init(params, LLAMA_EXAMPLE_DOWNLOAD); | |
| common_models_handler_apply(handler, params); | |
| } catch (const std::exception & e) { | |
| fprintf(stderr, "error: %s\n", e.what()); | |
| return 1; | |
| } | |
| if (!params.models_preset.empty()) { | |
| // -hf pointed at a preset repo: print the preset path and stop | |
| printf("%s\n", params.models_preset.c_str()); | |
| return 0; | |
| } | |
| if (params.model.path.empty()) { | |
| fprintf(stderr, "error: model download failed\n"); | |
| return 1; | |
| } | |
| if (!std::filesystem::exists(params.model.path)) { | |
| fprintf(stderr, "error: model file does not exist: %s\n", params.model.path.c_str()); | |
| return 1; | |
| } | |
| printf("%s\n", params.model.path.c_str()); | |
| if (!params.mmproj.path.empty()) { | |
| printf("%s\n", params.mmproj.path.c_str()); | |
| } | |
| if (!params.speculative.draft.mparams.path.empty()) { | |
| printf("%s\n", params.speculative.draft.mparams.path.c_str()); | |
| } | |
| return 0; | |
| } | |