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 bool run(llama_context * ctx, const common_params & params) { | |
| const llama_model * model = llama_get_model(ctx); | |
| const llama_vocab * vocab = llama_model_get_vocab(model); | |
| const bool add_bos = llama_vocab_get_add_bos(vocab); | |
| std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, add_bos, true); | |
| if (tokens.empty()) { | |
| LOG_ERR("%s : there are not input tokens to process - (try to provide a prompt with '-p')\n", __func__); | |
| return false; | |
| } | |
| LOG_INF("number of input tokens = %zu\n", tokens.size()); | |
| for (size_t i = 0; i < tokens.size(); ++i) { | |
| LOG_INF(" %d\n", tokens[i]); | |
| } | |
| if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()))) { | |
| LOG_ERR("%s : failed to eval\n", __func__); | |
| return false; | |
| } | |
| return true; | |
| } | |
| int main(int argc, char ** argv) { | |
| std::setlocale(LC_NUMERIC, "C"); | |
| common_debug_cb_user_data cb_data; | |
| common_params params; | |
| common_init(); | |
| if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) { | |
| return 1; | |
| } | |
| llama_backend_init(); | |
| llama_numa_init(params.numa); | |
| // pass the callback to the backend scheduler | |
| // it will be executed for each node during the graph computation | |
| params.cb_eval = common_debug_cb_eval; | |
| params.cb_eval_user_data = &cb_data; | |
| params.warmup = false; | |
| // init | |
| auto llama_init = common_init_from_params(params); | |
| auto * model = llama_init->model(); | |
| auto * ctx = llama_init->context(); | |
| if (model == nullptr || ctx == nullptr) { | |
| LOG_ERR("%s : failed to init\n", __func__); | |
| return 1; | |
| } | |
| // print system information | |
| { | |
| LOG_INF("\n"); | |
| LOG_INF("%s\n", common_params_get_system_info(params).c_str()); | |
| LOG_INF("\n"); | |
| } | |
| bool OK = run(ctx, params); | |
| if (!OK) { | |
| return 1; | |
| } | |
| LOG("\n"); | |
| llama_perf_context_print(ctx); | |
| llama_backend_free(); | |
| return 0; | |
| } | |