---
base_model: orangejuicesmith/Mythos-Nano-safetensor
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - qwen2
  - agentic-workflows
  - tool-calling
license: apache-2.0
language:
  - en
---

# TB-X1 — Mythos Nano

**TB-X1** is a model release in the TB-X series, developed by [Shadow0482](https://huggingface.co/Shadow0482), based on [Mythos Nano](https://huggingface.co/orangejuicesmith/Mythos-Nano-safetensor).

The model is distributed in **Safetensors format** and is intended for use with the Hugging Face Transformers ecosystem.

## Model Information

| Property | Details |
|---|---|
| Model | TB-X1 |
| Developer | Shadow0482 |
| Base model | Mythos Nano |
| Architecture | Qwen2 |
| Model format | Safetensors |
| Framework | Hugging Face Transformers |
| Optimization | Unsloth |
| Language | English |
| License | Apache 2.0 |

## Development Methodology

TB-X1 was developed using a teacher-guided, tool-driven agent workflow.

### Teacher Models

- **Claude Opus 5.5** — Teacher model for instruction and response generation.
- **GPT-6 Astra** — Teacher model for reasoning and workflow support.

### Agent Orchestration

The development workflow incorporates:

- Multi-step agent interactions.
- Tool-call orchestration.
- Teacher-guided instruction generation.
- Structured reasoning workflows.
- Model evaluation and refinement.

Approximately **500 million tool-call and agent-flow interactions** were used throughout the reported development workflow.

This figure represents workflow activity, not model parameters, training tokens, or dataset samples.

## Model Format

TB-X1 uses the Safetensors format.

Benefits include:

- Compatibility with Hugging Face Transformers.
- Efficient model weight loading.
- Support for GPU and CPU inference, depending on model architecture and hardware.
- Integration with fine-tuning and model optimization workflows.

## Usage

### Installation

```bash
pip install -U transformers torch accelerate safetensors
```

### Load with Transformers

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Shadow0482/tb-x1"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)

messages = [
    {"role": "user", "content": "Hello! Introduce yourself."}
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=256,
    temperature=0.7,
    do_sample=True
)

print(tokenizer.decode(
    outputs[0][inputs.shape[-1]:],
    skip_special_tokens=True
))
```

### Hugging Face Pipeline

```python
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="Shadow0482/tb-x1",
    device_map="auto"
)

result = pipe(
    "Explain how AI agents work.",
    max_new_tokens=256
)

print(result[0]["generated_text"])
```

## Compatibility

Designed for the Hugging Face ecosystem:

- Transformers
- Safetensors
- Text Generation Inference (TGI)
- PyTorch
- Accelerate
- Unsloth-compatible workflows

Actual compatibility depends on the model architecture, configuration, and runtime version.

## Acknowledgements

- **Base model:** [orangejuicesmith/Mythos-Nano-safetensor](https://huggingface.co/orangejuicesmith/Mythos-Nano-safetensor)
- **Optimization framework:** [Unsloth](https://github.com/unslothai/unsloth)
- **Teacher models:** Claude Opus 5.5 and GPT-6 Astra
- **Model ecosystem:** Hugging Face Transformers

## License

The base model metadata specifies the Apache 2.0 license. Please review the original model repository and applicable license terms before redistribution.

---

**TB-X Series**

Developed and maintained by [Shadow0482](https://huggingface.co/Shadow0482).