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 /components /app /dialogs /DialogConversationSelection.svelte

tda45

Upload folder using huggingface_hub (part 8)

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	<script lang="ts">
	import * as Dialog from '$lib/components/ui/dialog';
	import { ConversationSelection } from '$lib/components/app';

	interface Props {
	conversations: DatabaseConversation[];
	messageCountMap?: Map<string, number>;
	mode: 'export' \| 'import';
	onCancel: () => void;
	onConfirm: (selectedConversations: DatabaseConversation[]) => void;
	open?: boolean;
	}

	let {
	conversations,
	messageCountMap = new Map(),
	mode,
	onCancel,
	onConfirm,
	open = $bindable(false)
	}: Props = $props();

	let conversationSelectionRef: ConversationSelection \| undefined = $state();

	let previousOpen = $state(false);

	$effect(() => {
	if (open && !previousOpen && conversationSelectionRef) {
	conversationSelectionRef.reset();
	} else if (!open && previousOpen) {
	onCancel();
	}

	previousOpen = open;
	});
	</script>

	<Dialog.Root bind:open>
	<Dialog.Portal>
	<Dialog.Overlay class="z-[1000000]" />

	<Dialog.Content class="z-[1000001] max-w-2xl">
	<Dialog.Header>
	<Dialog.Title>
	Select Conversations to {mode === 'export' ? 'Export' : 'Import'}
	</Dialog.Title>

	<Dialog.Description>
	{#if mode === 'export'}
	Choose which conversations you want to export. Selected conversations will be downloaded
	as a JSON file.
	{:else}
	Choose which conversations you want to import. Selected conversations will be merged
	with your existing conversations.
	{/if}
	</Dialog.Description>
	</Dialog.Header>

	<ConversationSelection
	bind:this={conversationSelectionRef}
	{conversations}
	{messageCountMap}
	{mode}
	{onCancel}
	{onConfirm}
	/>
	</Dialog.Content>
	</Dialog.Portal>
	</Dialog.Root>