Instructions to use CodeStrux-Tech/tac-1-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CodeStrux-Tech/tac-1-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="CodeStrux-Tech/tac-1-gguf",
	filename="tac-1-Q5_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use CodeStrux-Tech/tac-1-gguf 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 CodeStrux-Tech/tac-1-gguf:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf CodeStrux-Tech/tac-1-gguf:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf CodeStrux-Tech/tac-1-gguf:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf CodeStrux-Tech/tac-1-gguf:Q5_K_M

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 CodeStrux-Tech/tac-1-gguf:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf CodeStrux-Tech/tac-1-gguf:Q5_K_M

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 CodeStrux-Tech/tac-1-gguf:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CodeStrux-Tech/tac-1-gguf:Q5_K_M

Use Docker

docker model run hf.co/CodeStrux-Tech/tac-1-gguf:Q5_K_M

LM Studio
Jan

vLLM

How to use CodeStrux-Tech/tac-1-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CodeStrux-Tech/tac-1-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CodeStrux-Tech/tac-1-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CodeStrux-Tech/tac-1-gguf:Q5_K_M

Ollama
How to use CodeStrux-Tech/tac-1-gguf with Ollama:
```
ollama run hf.co/CodeStrux-Tech/tac-1-gguf:Q5_K_M
```

Unsloth Studio

How to use CodeStrux-Tech/tac-1-gguf 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 CodeStrux-Tech/tac-1-gguf 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 CodeStrux-Tech/tac-1-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for CodeStrux-Tech/tac-1-gguf to start chatting

How to use CodeStrux-Tech/tac-1-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CodeStrux-Tech/tac-1-gguf:Q5_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "CodeStrux-Tech/tac-1-gguf:Q5_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use CodeStrux-Tech/tac-1-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CodeStrux-Tech/tac-1-gguf:Q5_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default CodeStrux-Tech/tac-1-gguf:Q5_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use CodeStrux-Tech/tac-1-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CodeStrux-Tech/tac-1-gguf:Q5_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "CodeStrux-Tech/tac-1-gguf:Q5_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use CodeStrux-Tech/tac-1-gguf with Docker Model Runner:
```
docker model run hf.co/CodeStrux-Tech/tac-1-gguf:Q5_K_M
```

Lemonade

How to use CodeStrux-Tech/tac-1-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CodeStrux-Tech/tac-1-gguf:Q5_K_M

Run and chat with the model

lemonade run user.tac-1-gguf-Q5_K_M

List all available models

lemonade list

tac-1-gguf — GGUF format for llama.cpp and Ollama

Overview

tac-1-gguf provides GGUF files quantized from CodeStrux-Tech/tac-1 using llama.cpp commit 67776ea.

file	size	sha256
`tac-1-Q5_K_M.gguf`	2.7 GB	`1c919ee74addda61bb6490195934b7c06b1a1793051e6119a2859d045e5b0634`
`tac-1-f16.gguf`	7.5 GB	`5ba9ce916778026da6a4970b41fe4f230b9a7c30c0f0437d3630724c7c94feea`

Serving

Ollama

ollama run hf.co/CodeStrux-Tech/tac-1-gguf:Q5_K_M

Ollama registers an HF-pulled model under the full hf.co/...:Q5_K_M name. To use it with the tico client (default model name tac-1), alias it once with ollama cp hf.co/CodeStrux-Tech/tac-1-gguf:Q5_K_M tac-1, or set TICO_OLLAMA_MODEL="hf.co/CodeStrux-Tech/tac-1-gguf:Q5_K_M".

llama.cpp server

llama-server -m tac-1-Q5_K_M.gguf --jinja

The server is OpenAI-compatible at /v1. Client env: OLLAMA_BASE_URL=http://localhost:8080/v1 TICO_OLLAMA_MODEL=tac-1.

Chat template warning

The chat template is ChatML with EOS id 151645 (<|im_end|>). There are 0 think references in the template. The template is byte-identical across the merged bf16, FP8, and GGUF builds. When using llama.cpp or Ollama, ensure the server applies the ChatML template correctly — the --jinja flag on llama-server enables this.

Training data attribution

Contains information from OpenStreetMap (https://www.openstreetmap.org/copyright), which is made available under the Open Database License (ODbL) 1.0. © OpenStreetMap contributors.

For full training details, architecture, evaluation, and limitations, see CodeStrux-Tech/tac-1.

Downloads last month: 29

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

5-bit

16-bit

Model tree for CodeStrux-Tech/tac-1-gguf

Base model

Qwen/Qwen3-4B-Instruct-2507

Finetuned

CodeStrux-Tech/tac-1

Quantized

(2)

this model