---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.3
tags:
- robotics
- autonomous-systems
- zeta
- geass-labs
- full-fine-tune
- sensor-fusion
- real-time-systems
language:
- en
datasets:
- mashleburneded/zeta-training-datasets
pipeline_tag: text-generation
---

# CC-Zeta-0: Autonomous Systems Language Model

<div align="center">
  <strong>8B parameter model specialized for robotics, autonomous systems, and real-time control</strong>
</div>

## Model Description

**CC-Zeta-0** is an 8B parameter language model specialized for autonomous systems, robotics, and real-time control applications. Built on Mistral-7B-v0.3, it underwent **full parameter fine-tuning** on domain-specific data covering sensor fusion, path planning, real-time constraints, and autonomous navigation.

Developed by **Geass Labs**, CC-Zeta-0 maintains strong general capabilities while excelling at technical discussions in robotics and autonomous systems domains.

Note on Persona Stability: This 8B iteration serves as an experimental telemetry bed for the upcoming 27B release. Users may encounter "identity drift" (hallucinated excerpts or external personas) due to residual noise in the raw training data. These artifacts have been mapped and are slated for removal via targeted alignment in the next publishment.

### Key Features

- **Domain Expertise**: Specialized knowledge in autonomous robotics, sensor fusion, real-time systems
- **Direct Communication**: Technical responses without unnecessary preambles or filler
- **Contextual Identity**: Maintains professional identity when relevant, natural responses otherwise
- **Full Fine-tune**: All 8B parameters trained (not LoRA), ensuring deep integration of domain knowledge
- **Production Ready**: Optimized for deployment in technical documentation, code assistance, and system design

### Performance Metrics

**Generation Speed**
- Throughput: ~37 tokens/sec (steady state)
- Hardware: AMD GPU with ROCm 6.2

**Benchmark Results**

| Benchmark | Score | Category | Comparison |
|-----------|-------|----------|------------|
| **MMLU** | **59.99%** | General Knowledge | Higher than base
| **HellaSwag** | **81.12%** | Commonsense Reasoning | Higher than base
| **Winogrande** | **75.22%** | Commonsense | Higher than base
| **ARC Challenge** | **52.65%** | Science Reasoning | Lower than base
| **GSM8K** | **40.64%** | Mathematical Reasoning | Higher than base
| **TruthfulQA** | **42.61%** | Truthfulness | Higher than base

**MMLU Domain Breakdown**
- Social Sciences: 70.49%
- General Knowledge: 67.69%
- Humanities: 53.99%
- STEM: 51.13%

## Training Details

### Training Data

- **Source**: [Zeta training datasets](https://huggingface.co/datasets/mashleburneded/zeta-training-datasets) (identity alignment, coding, physics, robotics and autonomous systems)
- **Size**: 1M curated examples
- **Format**: Conversational pairs covering technical scenarios
- **Quality**: Curated for accuracy, directness, and technical depth

### Training Configuration

- **Base Model**: `unsloth/Mistral-7B-v0.3`
- **Method**: Full parameter fine-tuning (all 8B parameters)
- **Duration**: 56 hours 48 minutes
- **Epochs**: 8 full epochs
- **Final Loss**: 0.0766
- **Hardware**: AMD GPU with ROCm 6.2
- **Precision**: bfloat16

### Training Hyperparameters

```yaml
Learning Rate: 2e-5 (cosine decay to ~1e-13)
Batch Size: 1 per device
Gradient Accumulation: 4 steps
Effective Batch Size: 4
Max Sequence Length: 2048 tokens
Optimizer: AdamW (8-bit)
Weight Decay: 0.01
Warmup Steps: 100
```

## Usage

### Basic Inference

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "geasslabs/CC-Zeta-0",
    device_map="auto",
    torch_dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained("geasslabs/CC-Zeta-0")

prompt = "Explain sensor fusion in autonomous vehicles"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Optimized Inference (4-bit Quantization)

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

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype="bfloat16"
)

model = AutoModelForCausalLM.from_pretrained(
    "geasslabs/CC-Zeta-0",
    quantization_config=quantization_config,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("geasslabs/CC-Zeta-0")
```

## Use Cases

### Ideal Applications

- **Technical Documentation**: Generate accurate robotics and autonomous systems documentation
- **Code Assistance**: Help with ROS, sensor drivers, control algorithms
- **System Design**: Discuss architecture for autonomous systems
- **Education**: Explain complex concepts in robotics and real-time systems
- **Research Support**: Assist with literature review and concept exploration

### Example Prompts

```
"What are the real-time constraints for autonomous navigation?"
"Explain Kalman filtering for sensor fusion"
"How do you handle dynamic obstacles in path planning?"
"Design a sensor fusion pipeline for a mobile robot"
"Implement a PID controller for robotic arm positioning"
```

## Limitations

- **Domain Focus**: Optimized for robotics/autonomous systems; may be less creative in unrelated domains
- **Recency**: Training data cutoff means recent developments may not be reflected
- **Verification**: Always verify technical claims and code in production environments
- **Scale**: 8B parameters provide strong performance but may not match larger models on highly complex reasoning

## Ethical Considerations

- **Autonomous Systems**: Use responsibly in safety-critical applications
- **Verification**: Always validate outputs in production robotics systems
- **Bias**: May reflect biases present in training data
- **Transparency**: Clearly indicate AI-generated content in documentation

## Citation

```bibtex
@misc{cc-zeta-0-2026,
  title={CC-Zeta-0: Autonomous Systems Language Model},
  author={Geass Labs},
  year={2026},
  publisher={HuggingFace},
  howpublished={\url{https://huggingface.co/geasslabs/CC-Zeta-0}}
}
```

## License

Apache 2.0

## Model Card Authors

**Geass Labs** - Autonomous Systems AI Research

---

*For questions, issues, or collaboration inquiries, please open an issue on the model repository.*


