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
| // | |
| // logging | |
| // | |
| LLAMA_ATTRIBUTE_FORMAT(2, 3) | |
| void llama_log_internal (ggml_log_level level, const char * format, ...); | |
| void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data); | |
| // | |
| // helpers | |
| // | |
| template <typename T> | |
| struct no_init { | |
| T value; | |
| no_init() = default; | |
| }; | |
| template <typename dst_t, typename src_t> | |
| static inline dst_t llama_cast(src_t v) { | |
| if constexpr (std::is_same_v<src_t, dst_t>) { | |
| return v; | |
| } else if constexpr (std::is_same_v<src_t, ggml_fp16_t> && std::is_same_v<dst_t, float>) { | |
| return ggml_fp16_to_fp32(v); | |
| } else if constexpr (std::is_same_v<src_t, float> && std::is_same_v<dst_t, ggml_fp16_t>) { | |
| return ggml_fp32_to_fp16(v); | |
| } else { | |
| static_assert(std::is_same_v<dst_t, void>, "unsupported type combination"); | |
| } | |
| } | |
| struct time_meas { | |
| time_meas(int64_t & t_acc, bool disable = false); | |
| ~time_meas(); | |
| const int64_t t_start_us; | |
| int64_t & t_acc; | |
| }; | |
| template <typename T> | |
| struct buffer_view { | |
| T * data; | |
| size_t size = 0; | |
| bool has_data() const { | |
| return data && size > 0; | |
| } | |
| }; | |
| void replace_all(std::string & s, const std::string & search, const std::string & replace); | |
| // TODO: rename to llama_format ? | |
| LLAMA_ATTRIBUTE_FORMAT(1, 2) | |
| std::string format(const char * fmt, ...); | |
| std::string llama_format_tensor_shape(const std::vector<int64_t> & ne); | |
| std::string llama_format_tensor_shape(const struct ggml_tensor * t); | |
| std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i); | |