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 /cache.ts

tda45

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	/**
	* Cache configuration constants
	*/

	/**
	* Default TTL (Time-To-Live) for cache entries in milliseconds
	* @default 5 minutes
	*/
	export const DEFAULT_CACHE_TTL_MS = 5 * 60 * 1000;

	/**
	* Default maximum number of entries in a cache
	* @default 100
	*/
	export const DEFAULT_CACHE_MAX_ENTRIES = 100;

	/**
	* TTL for model props cache in milliseconds
	* Props don't change frequently, so we can cache them longer
	* @default 10 minutes
	*/
	export const MODEL_PROPS_CACHE_TTL_MS = 10 * 60 * 1000;

	/**
	* Maximum number of model props to cache
	* @default 50
	*/
	export const MODEL_PROPS_CACHE_MAX_ENTRIES = 50;

	/**
	* Maximum number of MCP resources to cache
	* @default 50
	*/
	export const MCP_RESOURCE_CACHE_MAX_ENTRIES = 50;

	/**
	* TTL for MCP resource cache entries in milliseconds
	* @default 5 minutes
	*/
	export const MCP_RESOURCE_CACHE_TTL_MS = 5 * 60 * 1000;

	/**
	* Maximum number of inactive conversation states to keep in memory
	* States for conversations beyond this limit will be cleaned up
	* @default 10
	*/
	export const MAX_INACTIVE_CONVERSATION_STATES = 10;

	/**
	* Maximum age (in ms) for inactive conversation states before cleanup
	* States older than this will be removed during cleanup
	* @default 30 minutes
	*/
	export const INACTIVE_CONVERSATION_STATE_MAX_AGE_MS = 30 * 60 * 1000;