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
| // satisfies -Wmissing-declarations | |
| int llama_fit_params(int argc, char ** argv); | |
| int llama_fit_params(int argc, char ** argv) { | |
| common_params params; | |
| common_init(); | |
| if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_FIT_PARAMS)) { | |
| return 1; | |
| } | |
| llama_backend_init(); | |
| llama_numa_init(params.numa); | |
| auto mparams = common_model_params_to_llama(params); | |
| auto cparams = common_context_params_to_llama(params); | |
| if (!params.fit_params_print) { | |
| const common_params_fit_status status = common_fit_params(params.model.path.c_str(), &mparams, &cparams, | |
| params.tensor_split, params.tensor_buft_overrides.data(), params.fit_params_target.data(), params.fit_params_min_ctx, | |
| params.verbosity >= LOG_LEVEL_DEBUG ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR); | |
| if (status != COMMON_PARAMS_FIT_STATUS_SUCCESS) { | |
| LOG_ERR("%s: failed to fit CLI arguments to free memory, exiting...\n", __func__); | |
| exit(1); | |
| } | |
| LOG_INF("%s: printing fitted CLI arguments to stdout...\n", __func__); | |
| common_log_flush(common_log_main()); | |
| printf("-c %" PRIu32 " -ngl %" PRIi32, cparams.n_ctx, mparams.n_gpu_layers); | |
| size_t nd = llama_max_devices(); | |
| while (nd > 1 && mparams.tensor_split[nd - 1] == 0.0f) { | |
| nd--; | |
| } | |
| if (nd > 1) { | |
| for (size_t id = 0; id < nd; id++) { | |
| if (id == 0) { | |
| printf(" -ts "); | |
| } | |
| printf("%s%" PRIu32, id > 0 ? "," : "", uint32_t(mparams.tensor_split[id])); | |
| } | |
| } | |
| const size_t ntbo = llama_max_tensor_buft_overrides(); | |
| bool any_tbo = false; | |
| for (size_t itbo = 0; itbo < ntbo && mparams.tensor_buft_overrides[itbo].pattern != nullptr; itbo++) { | |
| if (itbo == 0) { | |
| printf(" -ot \""); | |
| } | |
| printf("%s%s=%s", itbo > 0 ? "," : "", mparams.tensor_buft_overrides[itbo].pattern, ggml_backend_buft_name(mparams.tensor_buft_overrides[itbo].buft)); | |
| any_tbo = true; | |
| } | |
| printf("%s\n", any_tbo ? "\"" : ""); | |
| } else { | |
| LOG_INF("%s: printing estimated memory in MiB to stdout (device, model, context, compute) ...\n", __func__); | |
| common_log_flush(common_log_main()); | |
| common_fit_print(params.model.path.c_str(), &mparams, &cparams); | |
| } | |
| return 0; | |
| } | |