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 /.devops /llama-cpp.srpm.spec

tda45

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	# SRPM for building from source and packaging an RPM for RPM-based distros.
	# https://docs.fedoraproject.org/en-US/quick-docs/creating-rpm-packages
	# Built and maintained by John Boero - boeroboy@gmail.com
	# In honor of Seth Vidal https://www.redhat.com/it/blog/thank-you-seth-vidal

	# Notes for llama.cpp:
	# 1. Tags are currently based on hash - which will not sort asciibetically.
	# We need to declare standard versioning if people want to sort latest releases.
	# In the meantime, YYYYMMDD format will be used.
	# 2. Builds for CUDA/OpenCL support are separate, with different depenedencies.
	# 3. NVidia's developer repo must be enabled with nvcc, cublas, clblas, etc installed.
	# Example: https://developer.download.nvidia.com/compute/cuda/repos/fedora37/x86_64/cuda-fedora37.repo
	# 4. OpenCL/CLBLAST support simply requires the ICD loader and basic opencl libraries.
	# It is up to the user to install the correct vendor-specific support.

	Name: llama.cpp
	Version: %( date "+%%Y%%m%%d" )
	Release: 1%{?dist}
	Summary: CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL)
	License: MIT
	Source0: https://github.com/ggml-org/llama.cpp/archive/refs/heads/master.tar.gz
	BuildRequires: coreutils make gcc-c++ git libstdc++-devel
	Requires: libstdc++
	URL: https://github.com/ggml-org/llama.cpp

	%define debug_package %{nil}
	%define source_date_epoch_from_changelog 0

	%description
	CPU inference for Meta's Lllama2 models using default options.
	Models are not included in this package and must be downloaded separately.

	%prep
	%setup -n llama.cpp-master

	%build
	make -j

	%install
	mkdir -p %{buildroot}%{_bindir}/
	cp -p llama-cli %{buildroot}%{_bindir}/llama-cli
	cp -p llama-completion %{buildroot}%{_bindir}/llama-completion
	cp -p llama-server %{buildroot}%{_bindir}/llama-server
	cp -p llama-simple %{buildroot}%{_bindir}/llama-simple

	mkdir -p %{buildroot}/usr/lib/systemd/system
	%{__cat} <<EOF > %{buildroot}/usr/lib/systemd/system/llama.service
	[Unit]
	Description=Llama.cpp server, CPU only (no GPU support in this build).
	After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target

	[Service]
	Type=simple
	EnvironmentFile=/etc/sysconfig/llama
	ExecStart=/usr/bin/llama-server $LLAMA_ARGS
	ExecReload=/bin/kill -s HUP $MAINPID
	Restart=never

	[Install]
	WantedBy=default.target
	EOF

	mkdir -p %{buildroot}/etc/sysconfig
	%{__cat} <<EOF > %{buildroot}/etc/sysconfig/llama
	LLAMA_ARGS="-m /opt/llama2/ggml-model-f32.bin"
	EOF

	%clean
	rm -rf %{buildroot}
	rm -rf %{_builddir}/*

	%files
	%{_bindir}/llama-cli
	%{_bindir}/llama-completion
	%{_bindir}/llama-server
	%{_bindir}/llama-simple
	/usr/lib/systemd/system/llama.service
	%config /etc/sysconfig/llama

	%pre

	%post

	%preun
	%postun

	%changelog