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 /tools /ui /src /lib /constants /model-id.ts

tda45

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	/** Sentinel value returned by `indexOf` when a substring is not found. */
	export const MODEL_ID_NOT_FOUND = -1;

	/** Separates `<org>` from `<model>` in a model ID, e.g. `org/ModelName`. */
	export const MODEL_ID_ORG_SEPARATOR = '/';

	/** Separates named segments within the model path, e.g. `ModelName-7B-GGUF`. */
	export const MODEL_ID_SEGMENT_SEPARATOR = '-';

	/** Separates the model path from the quantization tag, e.g. `model:Q4_K_M`. */
	export const MODEL_ID_QUANTIZATION_SEPARATOR = ':';

	/**
	* Matches a quantization/precision segment, e.g. `Q4_K_M`, `IQ4_XS`, `F16`, `BF16`, `MXFP4`.
	* Case-insensitive to handle both uppercase and lowercase inputs.
	*/
	export const MODEL_QUANTIZATION_SEGMENT_RE =
	/^(I?Q\d+(_[A-Z0-9]+)\|F\d+\|BF\d+\|MXFP\d+(_[A-Z0-9]+))$/i;

	/**
	* Matches prefix for custom quantization types, e.g. `UD-Q8_K_XL`.
	*/
	export const MODEL_CUSTOM_QUANTIZATION_PREFIX_RE = /^UD$/i;

	/**
	* Matches a parameter-count segment, e.g. `7B`, `1.5b`, `120M`.
	*/
	export const MODEL_PARAMS_RE = /^\d+(\.\d+)?[BbMmKkTt]$/;

	/**
	* Matches an activated-parameter-count segment, e.g. `A10B`, `a2.4b`.
	* The leading `A`/`a` distinguishes it from a regular params segment.
	*/
	export const MODEL_ACTIVATED_PARAMS_RE = /^[Aa]\d+(\.\d+)?[BbMmKkTt]$/;

	/**
	* Container format segments to exclude from tags (every model uses these).
	*/
	export const MODEL_IGNORED_SEGMENTS = new Set(['GGUF', 'GGML']);

	/**
	* Matches a trailing weight file extension, e.g. `model.gguf` -> `model`.
	*/
	export const MODEL_WEIGHT_EXTENSION_RE = /\.(gguf\|ggml)$/i;