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
| """ | |
| On-device bench and completion test runner for llama.cpp (CPU, GPU, NPU backends). | |
| On Android: calls upstream run-*.sh scripts from llama.cpp/scripts/snapdragon/adb/ | |
| on the QDC runner host (scripts wrap commands in ``adb shell`` internally). | |
| On Linux: runs llama-bench directly via run_linux.sh (BASH framework). | |
| Placeholders replaced at artifact creation time by run_qdc_jobs.py: | |
| <<MODEL_URL>> Direct URL to the GGUF model file (downloaded on-device) | |
| """ | |
| import os | |
| import subprocess | |
| import sys | |
| import pytest | |
| from utils import ( | |
| BIN_PATH, | |
| MODEL_DEVICE_PATH, | |
| MODEL_NAME, | |
| PROMPT_DIR, | |
| push_bundle_if_needed, | |
| run_adb_command, | |
| run_script, | |
| write_qdc_log, | |
| ) | |
| MODEL_URL = "<<MODEL_URL>>" | |
| def install(driver): | |
| push_bundle_if_needed(f"{BIN_PATH}/llama-cli") | |
| run_adb_command(f"mkdir -p /data/local/tmp/gguf {PROMPT_DIR}") | |
| run_adb_command(f"echo 'What is the capital of France?' > {PROMPT_DIR}/bench_prompt.txt") | |
| check = subprocess.run( | |
| ["adb", "shell", f"ls {MODEL_DEVICE_PATH}"], | |
| text=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, | |
| ) | |
| if check.returncode != 0: | |
| run_adb_command(f'curl -L -J --output {MODEL_DEVICE_PATH} "{MODEL_URL}"') | |
| def test_llama_completion(device): | |
| result = run_script( | |
| "run-completion.sh", | |
| extra_env={"D": device, "M": MODEL_NAME}, | |
| extra_args=["--batch-size", "128", "-n", "128", "--seed", "42", | |
| "-f", f"{PROMPT_DIR}/bench_prompt.txt"], | |
| ) | |
| write_qdc_log(f"llama_completion_{device}.log", result.stdout or "") | |
| assert result.returncode == 0, ( | |
| f"llama-completion {device} failed (exit {result.returncode})" | |
| ) | |
| _DEVICE_LOG_NAME = {"none": "cpu", "GPUOpenCL": "gpu", "HTP0": "htp"} | |
| def test_llama_bench(device): | |
| result = run_script( | |
| "run-bench.sh", | |
| extra_env={"D": device, "M": MODEL_NAME}, | |
| extra_args=["--batch-size", "128", "-p", "128", "-n", "32"], | |
| ) | |
| write_qdc_log(f"llama_bench_{_DEVICE_LOG_NAME[device]}.log", result.stdout or "") | |
| assert result.returncode == 0, ( | |
| f"llama-bench {device} failed (exit {result.returncode})" | |
| ) | |
| if __name__ == "__main__": | |
| ret = pytest.main(["-s", "--junitxml=results.xml", os.path.realpath(__file__)]) | |
| if os.path.exists("results.xml"): | |
| with open("results.xml") as f: | |
| write_qdc_log("results.xml", f.read()) | |
| sys.exit(ret) | |