Instructions to use Shadow0482/tb-x with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shadow0482/tb-x with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Shadow0482/tb-x")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Shadow0482/tb-x")
model = AutoModelForCausalLM.from_pretrained("Shadow0482/tb-x", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Shadow0482/tb-x 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 Shadow0482/tb-x:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Shadow0482/tb-x:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Shadow0482/tb-x:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Shadow0482/tb-x:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf Shadow0482/tb-x:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf Shadow0482/tb-x:Q4_K_M

Use Docker

docker model run hf.co/Shadow0482/tb-x:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use Shadow0482/tb-x with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Shadow0482/tb-x"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Shadow0482/tb-x",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Shadow0482/tb-x:Q4_K_M

SGLang

How to use Shadow0482/tb-x with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Shadow0482/tb-x" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Shadow0482/tb-x",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Shadow0482/tb-x" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Shadow0482/tb-x",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Shadow0482/tb-x with Ollama:
```
ollama run hf.co/Shadow0482/tb-x:Q4_K_M
```
Unsloth Desktop

How to use Shadow0482/tb-x with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Shadow0482/tb-x:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Shadow0482/tb-x:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Shadow0482/tb-x with Docker Model Runner:
```
docker model run hf.co/Shadow0482/tb-x:Q4_K_M
```

Lemonade

How to use Shadow0482/tb-x with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Shadow0482/tb-x:Q4_K_M

Run and chat with the model

lemonade run user.tb-x-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Shadow0482/tb-x with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Shadow0482/tb-x:Q4_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 Shadow0482/tb-x:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Shadow0482/tb-x with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Shadow0482/tb-x:Q4_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 "Shadow0482/tb-x:Q4_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"

TB-X — Mythos Nano GGUF

TB-X is a GGUF conversion of Mythos Nano, prepared using Unsloth and made compatible with the llama.cpp inference ecosystem.

The repository provides multiple GGUF precision and quantization variants, allowing users to choose an appropriate balance between model size, memory usage, inference performance, and output quality.

Model Information

Property	Details
Repository	`Shadow0482/tb-x`
Model name	TB-X
Base model	`orangejuicesmith/Mythos-Nano-safetensor`
Architecture tag	Qwen2
Format	GGUF
Conversion	Unsloth
Inference engine	llama.cpp
Language	English
License	Apache 2.0

Development & Agent Workflow

TB-X was developed with a teacher-guided, tool-driven agent workflow.

The development process used:

Claude Opus 5.5 — Teacher model.
GPT-6 Astra — Teacher and reasoning support.
Tool orchestration — Tool calls and agent interactions used throughout the workflow.

The workflow involved approximately 500 million tool-call and agent-flow interactions.

This figure describes the reported development workflow activity; it does not represent the model's parameter count, training tokens, or number of training samples.

The teacher models were used as part of the development workflow. They are not inference dependencies of the released GGUF files.

Available Model Files

TB-X includes the following GGUF variants:

File	Precision / Quantization
`Mythos-Nano-safetensor.BF16.gguf`	BF16
`Mythos-Nano-safetensor.F16.gguf`	F16
`Mythos-Nano-safetensor.Q2_K_L.gguf`	Q2_K_L
`Mythos-Nano-safetensor.Q3_K_M.gguf`	Q3_K_M
`Mythos-Nano-safetensor.Q4_0.gguf`	Q4_0
`Mythos-Nano-safetensor.Q4_1.gguf`	Q4_1
`Mythos-Nano-safetensor.Q4_K_M.gguf`	Q4_K_M
`Mythos-Nano-safetensor.Q5_K_M.gguf`	Q5_K_M
`Mythos-Nano-safetensor.Q6_K.gguf`	Q6_K
`Mythos-Nano-safetensor.Q8_0.gguf`	Q8_0

Choosing a Quantization

BF16 / F16: Higher precision, with greater memory requirements.
Q8_0: High-quality quantized option.
Q6_K / Q5_K_M: Good options when prioritizing quality with reduced memory usage.
Q4_K_M / Q4_0 / Q4_1: Practical lower-memory options.
Q3_K_M / Q2_K_L: More aggressive compression for constrained hardware.

Actual memory requirements depend on the architecture, context length, runtime, and inference configuration.

Usage with llama.cpp

Install or build a recent version of llama.cpp.

1. Text Generation

Run TB-X using the Hugging Face model repository:

llama-cli -hf Shadow0482/tb-x --jinja

2. Chat Template

The --jinja option enables llama.cpp's Jinja chat-template handling.

This is useful when the model repository provides a compatible chat template.

3. Run a Local GGUF File

Download your preferred quantization and run it directly:

llama-cli \
  -m Mythos-Nano-safetensor.Q4_K_M.gguf \
  --jinja \
  -cnv

Replace the filename with your selected GGUF variant.

4. OpenAI-Compatible API Server

Start a local inference server:

llama-server \
  -hf Shadow0482/tb-x \
  --jinja \
  --host 0.0.0.0 \
  --port 8080

The API is available at:

http://localhost:8080/v1

You can connect compatible applications and clients to the local server.

Compatibility

TB-X is designed for use with the llama.cpp ecosystem.

llama-cli
llama-server
OpenAI-compatible API clients
Compatible GGUF applications

Tool calling and agent functionality depend on the model's capabilities, chat template, and the runtime's tool-calling implementation. A GGUF conversion alone does not guarantee native function-calling support.

Credits & Acknowledgements

Base model: orangejuicesmith/Mythos-Nano-safetensor
Conversion framework: Unsloth
Inference engine: llama.cpp
Teacher models: Claude Opus 5.5 and GPT-6 Astra

License

The base model metadata specifies the Apache 2.0 license. Please review the base model's license and applicable conditions before redistributing or using derivative artifacts.