Instructions to use tda45/TdAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

TdAI / llama.cpp /.github /workflows /build-msys.yml

tda45

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	name: CI (msys)

	on:
	# only manual triggers due to low-importance of the workflows
	# TODO: for regular runs, provision dedicated self-hosted runners
	workflow_dispatch:
	# run once every week
	schedule:
	- cron: '0 0 * * 0'

	concurrency:
	group: ${{ github.workflow }}-${{ github.head_ref && github.ref \|\| github.run_id }}
	cancel-in-progress: true

	env:
	GGML_NLOOP: 3
	GGML_N_THREADS: 1
	LLAMA_ARG_LOG_COLORS: 1
	LLAMA_ARG_LOG_PREFIX: 1
	LLAMA_ARG_LOG_TIMESTAMPS: 1

	jobs:
	windows-msys2:
	runs-on: windows-2025

	strategy:
	fail-fast: false
	matrix:
	include:
	- { sys: UCRT64, env: ucrt-x86_64, compiler: gcc, build: Release }
	- { sys: CLANG64, env: clang-x86_64, compiler: clang, build: Release }

	steps:
	- name: Clone
	uses: actions/checkout@v6

	#- name: ccache
	# uses: ggml-org/ccache-action@v1.2.16
	# with:
	# key: msys-windows-2025-x64
	# variant: ccache
	# evict-old-files: 1d
	# save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

	- name: Setup ${{ matrix.sys }}
	uses: msys2/setup-msys2@cafece8e6baf9247cf9b1bf95097b0b983cc558d # v2
	with:
	update: true
	msystem: ${{matrix.sys}}
	install: >-
	mingw-w64-${{matrix.env}}-${{matrix.compiler}}
	mingw-w64-${{matrix.env}}-cmake
	mingw-w64-${{matrix.env}}-openblas

	- name: Build using CMake
	shell: msys2 {0}
	run: \|
	cmake -B build
	cmake --build build --config ${{ matrix.build }} -j $(nproc)

	- name: Clean after building using CMake
	shell: msys2 {0}
	run: \|
	rm -rf build

	- name: Build using CMake w/ OpenBLAS
	shell: msys2 {0}
	run: \|
	cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS
	cmake --build build --config ${{ matrix.build }} -j $(nproc)