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 argparse | |
| import sys | |
| from common import compare_tokens # type: ignore[import-not-found, ty:unresolved-import] | |
| def parse_arguments(): | |
| parser = argparse.ArgumentParser( | |
| description='Compare tokens between two models', | |
| formatter_class=argparse.RawDescriptionHelpFormatter, | |
| epilog=""" | |
| Examples: | |
| %(prog)s pytorch-gemma-3-270m-it llamacpp-gemma-3-270m-it-bf16 | |
| """ | |
| ) | |
| parser.add_argument( | |
| 'original', | |
| help='Original model name' | |
| ) | |
| parser.add_argument( | |
| 'converted', | |
| help='Converted model name' | |
| ) | |
| parser.add_argument( | |
| '-s', '--suffix', | |
| default='', | |
| help='Type suffix (e.g., "-embeddings")' | |
| ) | |
| parser.add_argument( | |
| '-d', '--data-dir', | |
| default='data', | |
| help='Directory containing token files (default: data)' | |
| ) | |
| parser.add_argument( | |
| '-v', '--verbose', | |
| action='store_true', | |
| help='Print prompts from both models' | |
| ) | |
| return parser.parse_args() | |
| def main(): | |
| args = parse_arguments() | |
| if args.verbose: | |
| from pathlib import Path | |
| data_dir = Path(args.data_dir) | |
| prompt1_file = data_dir / f"{args.original}{args.suffix}-prompt.txt" | |
| prompt2_file = data_dir / f"{args.converted}{args.suffix}-prompt.txt" | |
| if prompt1_file.exists(): | |
| print(f"\nOriginal model prompt ({args.original}):") | |
| print(f" {prompt1_file.read_text().strip()}") | |
| if prompt2_file.exists(): | |
| print(f"\nConverted model prompt ({args.converted}):") | |
| print(f" {prompt2_file.read_text().strip()}") | |
| print() | |
| result = compare_tokens( | |
| args.original, | |
| args.converted, | |
| type_suffix=args.suffix, | |
| output_dir=args.data_dir | |
| ) | |
| # Enable the script to be used in shell scripts so that they can check | |
| # the exit code for success/failure. | |
| sys.exit(0 if result else 1) | |
| if __name__ == "__main__": | |
| main() | |