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 os | |
| import hashlib | |
| logger = logging.getLogger("verify-checksum-models") | |
| def sha256sum(file): | |
| block_size = 16 * 1024 * 1024 # 16 MB block size | |
| b = bytearray(block_size) | |
| file_hash = hashlib.sha256() | |
| mv = memoryview(b) | |
| with open(file, 'rb', buffering=0) as f: | |
| while True: | |
| n = f.readinto(mv) | |
| if not n: | |
| break | |
| file_hash.update(mv[:n]) | |
| return file_hash.hexdigest() | |
| # Define the path to the llama directory (parent folder of script directory) | |
| llama_path = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir)) | |
| # Define the file with the list of hashes and filenames | |
| hash_list_file = os.path.join(llama_path, "SHA256SUMS") | |
| # Check if the hash list file exists | |
| if not os.path.exists(hash_list_file): | |
| logger.error(f"Hash list file not found: {hash_list_file}") | |
| exit(1) | |
| # Read the hash file content and split it into an array of lines | |
| with open(hash_list_file, "r") as f: | |
| hash_list = f.read().splitlines() | |
| # Create an array to store the results | |
| results = [] | |
| # Loop over each line in the hash list | |
| for line in hash_list: | |
| # Split the line into hash and filename | |
| hash_value, filename = line.split(" ") | |
| # Get the full path of the file by joining the llama path and the filename | |
| file_path = os.path.join(llama_path, filename) | |
| # Informing user of the progress of the integrity check | |
| logger.info(f"Verifying the checksum of {file_path}") | |
| # Check if the file exists | |
| if os.path.exists(file_path): | |
| # Calculate the SHA256 checksum of the file using hashlib | |
| file_hash = sha256sum(file_path) | |
| # Compare the file hash with the expected hash | |
| if file_hash == hash_value: | |
| valid_checksum = "V" | |
| file_missing = "" | |
| else: | |
| valid_checksum = "" | |
| file_missing = "" | |
| else: | |
| valid_checksum = "" | |
| file_missing = "X" | |
| # Add the results to the array | |
| results.append({ | |
| "filename": filename, | |
| "valid checksum": valid_checksum, | |
| "file missing": file_missing | |
| }) | |
| # Print column headers for results table | |
| print("filename".ljust(40) + "valid checksum".center(20) + "file missing".center(20)) # noqa: NP100 | |
| print("-" * 80) # noqa: NP100 | |
| # Output the results as a table | |
| for r in results: | |
| print(f"{r['filename']:40} {r['valid checksum']:^20} {r['file missing']:^20}") # noqa: NP100 | |