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
| ggml_backend_reg_t reg = NULL; | |
| ggml_backend_dev_t dev = NULL; | |
| ggml_backend_t bck = NULL; | |
| uint64_t timer_start = 0; | |
| uint64_t timer_total = 0; | |
| uint64_t timer_count = 0; | |
| uint32_t backend_dispatch_initialize(void * ggml_backend_reg_fct_p) { | |
| if (reg != NULL) { | |
| GGML_LOG_WARN(GGML_VIRTGPU_BCK "%s: already initialized\n", __func__); | |
| return APIR_BACKEND_INITIALIZE_ALREADY_INITED; | |
| } | |
| ggml_backend_reg_t (*ggml_backend_reg_fct)(void) = (ggml_backend_reg_t (*)()) ggml_backend_reg_fct_p; | |
| reg = ggml_backend_reg_fct(); | |
| if (reg == NULL) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: backend registration failed\n", __func__); | |
| return APIR_BACKEND_INITIALIZE_BACKEND_REG_FAILED; | |
| } | |
| size_t device_count = reg->iface.get_device_count(reg); | |
| if (!device_count) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: no device found\n", __func__); | |
| return APIR_BACKEND_INITIALIZE_NO_DEVICE; | |
| } | |
| dev = reg->iface.get_device(reg, 0); | |
| if (!dev) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: failed to get device\n", __func__); | |
| return APIR_BACKEND_INITIALIZE_NO_DEVICE; | |
| } | |
| bck = dev->iface.init_backend(dev, NULL); | |
| if (!bck) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: backend initialization failed\n", __func__); | |
| return APIR_BACKEND_INITIALIZE_BACKEND_INIT_FAILED; | |
| } | |
| return APIR_BACKEND_INITIALIZE_SUCCESS; | |
| } | |