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 /examples /sycl /README.md

tda45

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	# llama.cpp/example/sycl

	This example program provides the tools for llama.cpp for SYCL on Intel GPU.

	## Tool

	\|Tool Name\| Function\|Status\|
	\|-\|-\|-\|
	\|llama-ls-sycl-device\| List all SYCL devices with ID, compute capability, max work group size, etc.\|Support\|

	### llama-ls-sycl-device

	List all SYCL devices with ID, compute capability, max work group size, etc.

	1. Build the llama.cpp for SYCL for the specified target (using GGML_SYCL_TARGET).

	2. Enable oneAPI running environment (if GGML_SYCL_TARGET is set to INTEL -default-)

	```
	source /opt/intel/oneapi/setvars.sh
	```

	3. Execute

	```
	./build/bin/llama-ls-sycl-device
	```

	Check the ID in startup log, like:

	```
	found 2 SYCL devices:
	\| \| \| \| \|Max \| \|Max \|Global \| \|
	\| \| \| \| \|compute\|Max work\|sub \|mem \| \|
	\|ID\| Device Type\| Name\|Version\|units \|group \|group\|size \| Driver version\|
	\|--\|-------------------\|---------------------------------------\|-------\|-------\|--------\|-----\|-------\|---------------------\|
	\| 0\| [level_zero:gpu:0]\| Intel Arc A770 Graphics\| 1.3\| 512\| 1024\| 32\| 16225M\| 1.3.29138\|
	\| 1\| [level_zero:gpu:1]\| Intel UHD Graphics 750\| 1.3\| 32\| 512\| 32\| 62631M\| 1.3.29138\|

	```