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 /scripts /tool_bench.sh

tda45

Upload folder using huggingface_hub (part 6)

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	#!/usr/bin/env bash
	set -euo pipefail

	cmake --build build -j

	export LLAMA_CACHE=${LLAMA_CACHE:-$HOME/Library/Caches/llama.cpp}
	export LLAMA_SERVER_BIN_PATH=$PWD/build/bin/llama-server

	if [ ! -x "$LLAMA_SERVER_BIN_PATH" ]; then
	echo "Could not find llama-server binary at $LLAMA_SERVER_BIN_PATH"
	exit 1
	fi
	if [ ! -d "$LLAMA_CACHE" ]; then
	echo "Could not find llama cache at $LLAMA_CACHE, please set LLAMA_CACHE explicitly."
	exit 1
	fi

	export ARGS=(
	--llama-baseline="$(which llama-server)"
	--n 30
	--temp -1 # Leaves temperature parameter unset (use the server's default, e.g. 0.6 for ollama)
	--temp 0
	--temp 0.5
	--temp 0.75
	--temp 1
	--temp 1.5
	--temp 2
	--temp 5
	"$@"
	)

	./scripts/tool_bench.py run ${ARGS[@]} --model "Qwen 2.5 Coder 0.5B Q4_K_M" --output ../qwenc0.5b.jsonl --hf bartowski/Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M --ollama qwen2.5-coder:0.5b-instruct-q4_K_M
	./scripts/tool_bench.py run ${ARGS[@]} --model "Qwen 2.5 Coder 1.5B Q4_K_M" --output ../qwenc1.5b.jsonl --hf bartowski/Qwen2.5-Coder-1.5B-Instruct-GGUF:Q4_K_M --ollama qwen2.5-coder:1.5b-instruct-q4_K_M
	./scripts/tool_bench.py run ${ARGS[@]} --model "Qwen 2.5 Coder 3B Q4_K_M" --output ../qwenc3b.jsonl --hf bartowski/Qwen2.5-Coder-3B-Instruct-GGUF:Q4_K_M --ollama qwen2.5-coder:3b-instruct-q4_K_M
	./scripts/tool_bench.py run ${ARGS[@]} --model "Qwen 2.5 Coder 7B Q4_K_M" --output ../qwenc7b.jsonl --hf bartowski/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M --ollama qwen2.5-coder:7b-instruct-q4_K_M
	./scripts/tool_bench.py run ${ARGS[@]} --model "Qwen 2.5 Coder 32B Q4_K_M" --output ../qwenc32b.jsonl --hf bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M --ollama qwen2.5-coder:32B-instruct-q4_K_M
	./scripts/tool_bench.py run ${ARGS[@]} --model "Qwen 2.5 1.5B Q4_K_M" --output ../qwen1.5b.jsonl --hf bartowski/Qwen2.5-1.5B-Instruct-GGUF:Q4_K_M --ollama qwen2.5:1.5b-instruct-q4_K_M
	./scripts/tool_bench.py run ${ARGS[@]} --model "Qwen 2.5 3B Q4_K_M" --output ../qwen3b.jsonl --hf bartowski/Qwen2.5-3B-Instruct-GGUF:Q4_K_M --ollama qwen2.5:3b-instruct-q4_K_M
	./scripts/tool_bench.py run ${ARGS[@]} --model "Qwen 2.5 7B Q4_K_M" --output ../qwen7b.jsonl --hf bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M --ollama qwen2.5:7b-instruct-q4_K_M

	./scripts/tool_bench.py run ${ARGS[@]} --model "Llama 3.2 Instruct 1B Q4_K_M" --output ../llama1b.jsonl --hf bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M --ollama llama3.2:1b-instruct-q4_K_M
	./scripts/tool_bench.py run ${ARGS[@]} --model "Llama 3.2 Instruct 3B Q4_K_M" --output ../llama3b.jsonl --hf bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M --ollama llama3.2:3b-instruct-q4_K_M
	./scripts/tool_bench.py run ${ARGS[@]} --model "Llama 3.1 Instruct 8B Q4_K_M" --output ../llama8b.jsonl --hf bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M --ollama llama3.1:8b-instruct-q4_K_M
	./scripts/tool_bench.py run ${ARGS[@]} --model "Llama 3.3 70B Q4_K_M" --output ../llama70b.jsonl --hf bartowski/Llama-3.3-70B-Instruct-GGUF:Q4_K_M

	./scripts/tool_bench.py run ${ARGS[@]} --model "Mistral Nemo Q4_K_M" --output ../nemo.jsonl --hf bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M --ollama mistral-nemo:12b-instruct-2407-q4_K_M

	./scripts/tool_bench.py run ${ARGS[@]} --model "Hermes 3 Llama 3.1 8B Q4_K_M" --output ../hermes3.jsonl --hf bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M --ollama hermes3:8b-llama3.1-q4_K_M --chat-template-file <( python scripts/get_chat_template.py NousResearch/Hermes-3-Llama-3.1-8B tool_use )
	./scripts/tool_bench.py run ${ARGS[@]} --model "Hermes 2 Pro Llama 3 8B Q4_K_M" --output ../hermes2.jsonl --hf bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M --ollama hermes2:8b-llama3-q4_K_M --chat-template-file <( python scripts/get_chat_template.py NousResearch/Hermes-2-Pro-Llama-3-8B tool_use )

	./scripts/tool_bench.py run ${ARGS[@]} --model "Functionary Small V3.2 Q4_K_M" --output ../funct3.2.jsonl --hf bartowski/functionary-small-v3.2-GGUF:Q4_K_M
	./scripts/tool_bench.py run ${ARGS[@]} --model "FireFunction V2 IQ1_M" --output ../firef2.jsonl --hf bartowski/firefunction-v2-GGUF:IQ1_M --chat-template-file <( python scripts/get_chat_template.py fireworks-ai/llama-3-firefunction-v2 tool_use )

	./scripts/tool_bench.py run ${ARGS[@]} --model "Command R7B 12-2024 Q6_K_L" --output ../c4ai.jsonl --hf bartowski/c4ai-command-r7b-12-2024-GGUF:Q6_K_L --chat-template-file <( python scripts/get_chat_template.py CohereForAI/c4ai-command-r7b-12-2024 tool_use )

	./scripts/tool_bench.py run ${ARGS[@]} --model "Gemma 2 2B Q8_0" --output ../gemma2.jsonl --hf bartowski/gemma-2-2b-it-GGUF:Q8_0
	./scripts/tool_bench.py run ${ARGS[@]} --model "Phi 4 Instruct Q4_K_M" --output ../phi4.jsonl --hf bartowski/phi-4-GGUF:Q4_K_M # --ollama phi4
	./scripts/tool_bench.py run ${ARGS[@]} --model "Phi 3.5 Mini Instruct Q4_K_M" --output ../phi3.5.jsonl --hf bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M # --ollama phi3.5:3.8b-mini-instruct-q4_K_M

	# ./scripts/tool_bench.py run ${ARGS[@]} --model "DeepSeek R1 Distill Qwen 7B Q6_K_L" --output ../dsqw7.jsonl --hf bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q6_K_L --chat-template-file <( python scripts/get_chat_template.py NousResearch/DeepSeek-R1-Distill-Qwen-7B tool_use )
	# ./scripts/tool_bench.py run ${ARGS[@]} --model "DeepSeek R1 Distill Qwen 32B Q4_K_M" --output ../dsqw32.jsonl --hf bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF:Q4_K_M --chat-template-file <( python scripts/get_chat_template.py NousResearch/DeepSeek-R1-Distill-Qwen-32B tool_use )


	for f in ../*.jsonl; do
	./scripts/tool_bench.py plot "$f" --output ${f%.jsonl}.png \|\| true
	done