Instructions to use Shadow0482/tb-x1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shadow0482/tb-x1 with Transformers:

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

pipe = pipeline("text-generation", model="Shadow0482/tb-x1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("Shadow0482/tb-x1")
model = AutoModelForCausalLM.from_pretrained("Shadow0482/tb-x1", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Shadow0482/tb-x1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Shadow0482/tb-x1"
# 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-x1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Shadow0482/tb-x1

SGLang

How to use Shadow0482/tb-x1 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-x1" \
    --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-x1",
		"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-x1" \
        --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-x1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use Shadow0482/tb-x1 with Docker Model Runner:
```
docker model run hf.co/Shadow0482/tb-x1
```

tb-x1 / README.md

Shadow0482

Update README.md

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---
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).

	---
	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).