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-cann.yml

tda45

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	name: CI (cann)

	on:
	workflow_dispatch: # allows manual triggering
	push:
	branches:
	- master
	paths: [
	'.github/workflows/build-cann.yml',
	'**/CMakeLists.txt',
	'**/.cmake',
	'*/.h',
	'*/.hpp',
	'*/.c',
	'*/.cpp'
	]

	pull_request:
	types: [opened, synchronize, reopened]
	paths: [
	'.github/workflows/build-cann.yml',
	'ggml/src/ggml-cann/**'
	]

	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:
	# TODO: this build is disabled to save Github Actions resources (https://github.com/ggml-org/llama.cpp/pull/23705)
	# in order to enable it again, we have to provision dedicated runners to run it
	# openEuler-latest-cann:
	# defaults:
	# run:
	# shell: bash -el {0}
	# strategy:
	# matrix:
	# arch: [x86, aarch64]
	# chip_type: ['910b', '310p']
	# build: ['Release']
	# use_acl_graph: ['on', 'off']
	# exclude:
	# # 310P does not support USE_ACL_GRAPH=on
	# - chip_type: '310p'
	# use_acl_graph: 'on'
	# runs-on: ${{ matrix.arch == 'aarch64' && 'ubuntu-24.04-arm' \|\| 'ubuntu-24.04' }}
	# steps:
	# - name: Checkout
	# uses: actions/checkout@v6
	# with:
	# fetch-depth: 0
	#
	# - name: Free up disk space
	# uses: ggml-org/free-disk-space@v1.3.1
	# with:
	# tool-cache: true
	#
	# - name: Set container image
	# id: cann-image
	# run: \|
	# image="ascendai/cann:${{ matrix.chip_type == '910b' && '8.5.0-910b-openeuler24.03-py3.11' \|\| '8.5.0-310p-openeuler24.03-py3.11' }}"
	# echo "image=${image}" >> "${GITHUB_OUTPUT}"
	#
	# - name: Pull container image
	# run: docker pull "${{ steps.cann-image.outputs.image }}"
	#
	# - name: Build
	# env:
	# BUILD_TYPE: ${{ matrix.build }}
	# SOC_TYPE: ascend${{ matrix.chip_type }}
	# USE_ACL_GRAPH: ${{ matrix.use_acl_graph }}
	# run: \|
	# HOST_UID=$(id -u)
	# HOST_GID=$(id -g)
	#
	# docker run --rm \
	# -v "${PWD}:/workspace" \
	# -w /workspace \
	# -e SOC_TYPE=${SOC_TYPE} \
	# -e BUILD_TYPE=${BUILD_TYPE} \
	# -e USE_ACL_GRAPH=${USE_ACL_GRAPH} \
	# "${{ steps.cann-image.outputs.image }}" \
	# bash -lc '
	# set -e
	# yum install -y --setopt=install_weak_deps=False --setopt=tsflags=nodocs git gcc gcc-c++ make cmake openssl-devel
	# yum clean all && rm -rf /var/cache/yum
	# git config --global --add safe.directory "/workspace"
	# export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH}
	# cmake -S . -B build \
	# -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \
	# -DGGML_CANN=on \
	# -DSOC_TYPE=${SOC_TYPE} \
	# -DUSE_ACL_GRAPH=${USE_ACL_GRAPH}
	# cmake --build build -j $(nproc)
	#
	# chown -R '"${HOST_UID}"':'"${HOST_GID}"' /workspace/build
	# '