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

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.