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.
- 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
- 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
- Atomic Chat
File size: 2,425 Bytes
1debbd5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | #!/usr/bin/env python3
from huggingface_hub import HfApi
import argparse
import sys
def add_model_to_collection(collection_slug, model_id, note=""):
"""
Add a model to an existing collection
Args:
collection_slug: The slug of the collection (e.g., "username/collection-name-12345")
model_id: The model repository ID (e.g., "username/model-name")
note: Optional note about the model
Returns:
True if successful, False if failed
"""
# Initialize API
api = HfApi()
try:
user_info = api.whoami()
print(f"β
Authenticated as: {user_info['name']}")
# Verify the model exists
print(f"π Checking if model exists: {model_id}")
try:
model_info = api.model_info(model_id)
except Exception as e:
print(f"β Model not found or not accessible: {model_id}")
print(f"Error: {e}")
return False
print(f"π Adding model to collection...")
api.add_collection_item(
collection_slug=collection_slug,
item_id=model_id,
item_type="model",
note=note
)
print(f"β
Model added to collection successfully!")
print(f"π Collection URL: https://huggingface.co/collections/{collection_slug}")
return True
except Exception as e:
print(f"β Error adding model to collection: {e}")
return False
def main():
# This script requires that the environment variable HF_TOKEN is set with your
# Hugging Face API token.
api = HfApi()
parser = argparse.ArgumentParser(description='Add model to a Huggingface Collection')
parser.add_argument('--collection', '-c', help='The collection slug username/collection-hash', required=True)
parser.add_argument('--model', '-m', help='The model to add to the Collection', required=True)
parser.add_argument('--note', '-n', help='An optional note/description', required=False)
args = parser.parse_args()
collection = args.collection
model = args.model
note = args.note
success = add_model_to_collection(
collection_slug=collection,
model_id=model,
note=note
)
if success:
print("\nπ Model added successfully!")
else:
print("\nβ Failed to add model to collection")
sys.exit(1)
if __name__ == "__main__":
main()
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