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
| // | |
| // INI preset parser and writer | |
| // | |
| constexpr const char * COMMON_PRESET_DEFAULT_NAME = "default"; | |
| struct common_preset_context; | |
| struct common_preset { | |
| std::string name; | |
| // options are stored as common_arg to string mapping, representing CLI arg and its value | |
| std::map<common_arg, std::string> options; | |
| // convert preset to CLI argument list | |
| std::vector<std::string> to_args(const std::string & bin_path = "") const; | |
| // convert preset to INI format string | |
| std::string to_ini() const; | |
| // TODO: maybe implement to_env() if needed | |
| // modify preset options where argument is identified by its env variable | |
| void set_option(const common_preset_context & ctx, const std::string & env, const std::string & value); | |
| // unset option by its env variable | |
| void unset_option(const std::string & env); | |
| // get option value by its env variable, return false if not found | |
| bool get_option(const std::string & env, std::string & value) const; | |
| // merge another preset into this one, overwriting existing options | |
| void merge(const common_preset & other); | |
| // apply preset options to common_params | |
| // optionally specify handled_keys to only apply a subset of options (identified by their env), if empty, apply all options | |
| void apply_to_params(common_params & params, const std::set<std::string> & handled_keys = std::set<std::string>()) const; | |
| }; | |
| // interface for multiple presets in one file | |
| using common_presets = std::map<std::string, common_preset>; | |
| // context for loading and editing presets | |
| struct common_preset_context { | |
| common_params default_params; // unused for now | |
| common_params_context ctx_params; | |
| std::map<std::string, common_arg> key_to_opt; | |
| bool filter_allowed_keys = false; | |
| std::set<std::string> allowed_keys; | |
| // if only_remote_allowed is true, only accept whitelisted keys | |
| common_preset_context(llama_example ex); | |
| // load presets from INI file | |
| common_presets load_from_ini(const std::string & path, common_preset & global) const; | |
| // generate presets from cached models | |
| common_presets load_from_cache() const; | |
| // generate presets from local models directory | |
| // for the directory structure, see "Using multiple models" in server/README.md | |
| common_presets load_from_models_dir(const std::string & models_dir) const; | |
| // generate one preset from CLI arguments | |
| common_preset load_from_args(int argc, char ** argv) const; | |
| // cascade multiple presets if exist on both: base < added | |
| // if preset does not exist in base, it will be added without modification | |
| common_presets cascade(const common_presets & base, const common_presets & added) const; | |
| // apply presets over a base preset (same idea as CSS cascading) | |
| common_presets cascade(const common_preset & base, const common_presets & presets) const; | |
| }; | |