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 /model-conversion /scripts /embedding /run-converted-model.sh

tda45

Upload folder using huggingface_hub (part 3)

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	#!/usr/bin/env bash

	set -e

	# Parse command line arguments
	CONVERTED_MODEL=""
	PROMPTS_FILE=""
	EMBD_NORMALIZE="2"

	while [[ $# -gt 0 ]]; do
	case $1 in
	-p\|--prompts-file)
	PROMPTS_FILE="$2"
	shift 2
	;;
	--embd-normalize)
	EMBD_NORMALIZE="$2"
	shift 2
	;;
	*)
	if [ -z "$CONVERTED_MODEL" ]; then
	CONVERTED_MODEL="$1"
	fi
	shift
	;;
	esac
	done

	# First try command line argument, then environment variable
	CONVERTED_MODEL="${CONVERTED_MODEL:-"$CONVERTED_EMBEDDING_MODEL"}"
	BUILD_DIR="${BUILD_DIR:-"../../build"}"

	# Final check if we have a model path
	if [ -z "$CONVERTED_MODEL" ]; then
	echo "Error: Model path must be provided either as:" >&2
	echo " 1. Command line argument" >&2
	echo " 2. CONVERTED_EMBEDDING_MODEL environment variable" >&2
	exit 1
	fi

	# Read prompt from file or use default
	if [ -n "$PROMPTS_FILE" ]; then
	if [ ! -f "$PROMPTS_FILE" ]; then
	echo "Error: Prompts file '$PROMPTS_FILE' not found" >&2
	exit 1
	fi
	PROMPT=$(cat "$PROMPTS_FILE")
	else
	PROMPT="Hello world today"
	fi

	echo $CONVERTED_MODEL

	cmake --build ${BUILD_DIR} --target llama-debug -j8
	${BUILD_DIR}/bin/llama-debug -m "$CONVERTED_MODEL" --embedding -p "$PROMPT" --save-logits --embd-normalize $EMBD_NORMALIZE