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
| #!/usr/bin/env python3 | |
| import logging | |
| import sys | |
| from pathlib import Path | |
| logger = logging.getLogger("reader") | |
| # Necessary to load the local gguf package | |
| sys.path.insert(0, str(Path(__file__).parent.parent)) | |
| from gguf.gguf_reader import GGUFReader | |
| def read_gguf_file(gguf_file_path): | |
| """ | |
| Reads and prints key-value pairs and tensor information from a GGUF file in an improved format. | |
| Parameters: | |
| - gguf_file_path: Path to the GGUF file. | |
| """ | |
| reader = GGUFReader(gguf_file_path) | |
| # List all key-value pairs in a columnized format | |
| print("Key-Value Pairs:") # noqa: NP100 | |
| max_key_length = max(len(key) for key in reader.fields.keys()) | |
| for key, field in reader.fields.items(): | |
| value = field.parts[field.data[0]] | |
| print(f"{key:{max_key_length}} : {value}") # noqa: NP100 | |
| print("----") # noqa: NP100 | |
| # List all tensors | |
| print("Tensors:") # noqa: NP100 | |
| tensor_info_format = "{:<30} | Shape: {:<15} | Size: {:<12} | Quantization: {}" | |
| print(tensor_info_format.format("Tensor Name", "Shape", "Size", "Quantization")) # noqa: NP100 | |
| print("-" * 80) # noqa: NP100 | |
| for tensor in reader.tensors: | |
| shape_str = "x".join(map(str, tensor.shape)) | |
| size_str = str(tensor.n_elements) | |
| quantization_str = tensor.tensor_type.name | |
| print(tensor_info_format.format(tensor.name, shape_str, size_str, quantization_str)) # noqa: NP100 | |
| if __name__ == '__main__': | |
| if len(sys.argv) < 2: | |
| logger.info("Usage: reader.py <path_to_gguf_file>") | |
| sys.exit(1) | |
| gguf_file_path = sys.argv[1] | |
| read_gguf_file(gguf_file_path) | |