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 /utils /quantize.sh

tda45

Upload folder using huggingface_hub (part 3)

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	#!/usr/bin/env bash

	set -e

	CONVERTED_MODEL="${1:-"$CONVERTED_MODEL"}"
	QUANTIZED_TYPE="${2:-"$QUANTIZED_TYPE"}"
	TOKEN_EMBD_TYPE="${3:-"${TOKEN_EMBD_TYPE}"}"
	OUTPUT_TYPE="${4:-"${OUTPUT_TYPE}"}"
	BUILD_DIR="${5:-"$BUILD_DIR"}"
	QUANTIZED_MODEL=$CONVERTED_MODEL

	# 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_MODEL environment variable" >&2
	exit 1
	fi

	if [ -z "$QUANTIZED_TYPE" ]; then
	echo "Error: QUANTIZED_TYPE is required" >&2
	exit 1
	fi

	echo $CONVERTED_MODEL

	# Process the quantized model filename
	if [[ "$QUANTIZED_MODEL" == *.gguf ]]; then
	# Remove .gguf suffix, add quantized type, then add .gguf back
	BASE_NAME="${QUANTIZED_MODEL%.gguf}"
	QUANTIZED_MODEL="${BASE_NAME}-${QUANTIZED_TYPE}.gguf"
	else
	echo "Error: QUANTIZED_MODEL must end with .gguf extension" >&2
	exit 1
	fi

	if [ -z "$BUILD_DIR" ]; then
	BUILD_DIR="../../build"
	fi

	cmake --build $BUILD_DIR --target llama-quantize -j8

	echo $TOKEN_EMBD_TYPE
	echo $OUTPUT_TYPE

	CMD_ARGS=("${BUILD_DIR}/bin/llama-quantize")
	[[ -n "$TOKEN_EMBD_TYPE" ]] && CMD_ARGS+=("--token-embedding-type" "$TOKEN_EMBD_TYPE")
	[[ -n "$OUTPUT_TYPE" ]] && CMD_ARGS+=("--output-tensor-type" "$OUTPUT_TYPE")
	CMD_ARGS+=("$CONVERTED_MODEL" "$QUANTIZED_MODEL" "$QUANTIZED_TYPE")

	"${CMD_ARGS[@]}"

	echo "Quantized model saved to: $QUANTIZED_MODEL"