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 | |
| from huggingface_hub import HfApi | |
| import argparse | |
| # This script requires that the environment variable HF_TOKEN is set with your | |
| # Hugging Face API token. | |
| api = HfApi() | |
| def load_template_and_substitute(template_path, **kwargs): | |
| try: | |
| with open(template_path, 'r', encoding='utf-8') as f: | |
| template_content = f.read() | |
| return template_content.format(**kwargs) | |
| except FileNotFoundError: | |
| print(f"Template file '{template_path}' not found!") | |
| return None | |
| except KeyError as e: | |
| print(f"Missing template variable: {e}") | |
| return None | |
| parser = argparse.ArgumentParser(description='Create a new Hugging Face model repository') | |
| parser.add_argument('--model-name', '-m', help='Name for the model', required=True) | |
| parser.add_argument('--namespace', '-ns', help='Namespace to add the model to', required=True) | |
| parser.add_argument('--org-base-model', '-b', help='Original Base model name', default="") | |
| parser.add_argument('--no-card', action='store_true', help='Skip creating model card') | |
| parser.add_argument('--private', '-p', action='store_true', help='Create private model') | |
| parser.add_argument('--embedding', '-e', action='store_true', help='Use embedding model card template') | |
| parser.add_argument('--dry-run', '-d', action='store_true', help='Print repository info and template without creating repository') | |
| args = parser.parse_args() | |
| repo_id = f"{args.namespace}/{args.model_name}-GGUF" | |
| print("Repository ID: ", repo_id) | |
| repo_url = None | |
| if not args.dry_run: | |
| repo_url = api.create_repo( | |
| repo_id=repo_id, | |
| repo_type="model", | |
| private=args.private, | |
| exist_ok=False | |
| ) | |
| if not args.no_card: | |
| if args.embedding: | |
| template_path = "scripts/embedding/modelcard.template" | |
| else: | |
| template_path = "scripts/causal/modelcard.template" | |
| print("Template path: ", template_path) | |
| model_card_content = load_template_and_substitute( | |
| template_path, | |
| model_name=args.model_name, | |
| namespace=args.namespace, | |
| base_model=args.org_base_model, | |
| ) | |
| if args.dry_run: | |
| print("\nTemplate Content:\n") | |
| print(model_card_content) | |
| else: | |
| if model_card_content: | |
| api.upload_file( | |
| path_or_fileobj=model_card_content.encode('utf-8'), | |
| path_in_repo="README.md", | |
| repo_id=repo_id | |
| ) | |
| print("Model card created successfully.") | |
| else: | |
| print("Failed to create model card.") | |
| if not args.dry_run and repo_url: | |
| print(f"Repository created: {repo_url}") | |