CC-Zeta-0: Autonomous Systems Language Model

8B parameter model specialized for robotics, autonomous systems, and real-time control

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 (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

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

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)

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

@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

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