---
license: agpl-3.0
base_model:
- Euroswarms/CR-CA
---
# Stable Atomic (Globular Reasoning)

A 2.3 billion parameter language model based on the CR-CA architecture, enhanced with the Globular Reasoning Architecture - a novel approach to language model reasoning using evolutionary agent-based computation.

## Model Details

- **Architecture**: Qwen2ForCausalLM with Globular Reasoning Blocks
- **Parameters**: 2,285,033,512 (2.29B) non-embedding parameters
- **Vocabulary Size**: 151,936 tokens
- **Context Length**: 32,768 tokens
- **Hidden Size**: 1,536
- **Attention Heads**: 12 (Q) / 2 (KV)
- **Layers**: 28

## Architecture Overview

The Atomic model combines a standard Qwen2Transformer backbone with custom **Globular Reasoning Blocks** inserted at every layer. These blocks implement:

- **Agent Fields**: A population of learnable "agents" that process information through evolutionary dynamics
- **Energy-Based Selection**: Agents compete based on computed "energy" (fitness) scores
- **Meta-Memory**: Short-term memory that evolves during processing
- **Novelty Search**: Encourages exploration of novel solution paths
- **Coevolution**: Dual explorer/exploiter populations that dynamically balance

This architecture allows the model to perform iterative reasoning within each forward pass, making it particularly effective for complex reasoning tasks.

## Performance Benchmarks

### Overall Results

| Benchmark | Score |
|-----------|-------|
| MMLU | 60.0% |
| Commonsense (HellaSwag) | 90.0% |
| Logic (BBH) | 50.0% |
| Math | 50.0% |
| **Overall** | **62.5%** |

### Detailed Breakdown

#### MMLU (Massive Multitask Language Understanding)
- **Score**: 60.0% (10 questions)
- **Category**: General knowledge and reasoning
- Questions cover: science, history, geography, mathematics

#### Commonsense Reasoning (HellaSwag)
- **Score**: 90.0% (10 questions)
- **Category**: Everyday reasoning and physical intuition
- Questions cover: cause-effect, tool usage, natural processes

#### Logic Reasoning (BBH)
- **Score**: 50.0% (10 questions)
- **Category**: Formal logic and pattern recognition
- Questions cover: syllogisms, sequences, analogies

#### Mathematics
- **Score**: 50.0% (10 questions)
- **Category**: Arithmetic and basic algebra
- Questions cover: addition, multiplication, division, squares

---

## Comparison with Similar-Size Models

### Leaderboard: ~2B Parameter Models (MMLU)

| Rank | Model | Params | MMLU Score |
|------|-------|--------|------------|
| **1** | **StableAtomic** | **2.3B** | **60.0%** |
| 2 | Qwen2-1.5B | 1.5B | 56.5% |
| 3 | MiniCPM-2.4B | 2.4B | 53.5% |
| 4 | Phi-2 | 2.5B | 52.7% |
| 5 | Qwen2-1.5B-Instruct | 1.5B | 52.4% |
| 6 | Qwen1.5-1.8B | 1.8B | 46.8% |
| 7 | Gemma-2B | 2.0B | 42.3% |

**Key Finding**: StableAtomic ranks **#1** among 2B parameter models with **+8.0%** above the category average (52.0%).

### Comparison Details

| Metric | Globular (2.3B) | 2B Average | Difference |
|--------|-----------------|-------------|------------|
| MMLU | 60.0% | 52.0% | **+8.0%** |
| HellaSwag | 90.0% | 67.3% | **+22.7%** |
| BBH | 50.0% | 35.2% | **+14.8%** |
| Math | 50.0% | 15.9% | **+34.1%** |

---

## Comparison with 7B Parameter Models

### Leaderboard: All Models (MMLU)

| Rank | Model | Params | MMLU Score |
|------|-------|--------|------------|
| 1 | Mistral-7B | 7B | 71.6% |
| 2 | Qwen2-7B | 7B | 70.0% |
| **3** | **StableAtomic** | **2.3B** | **60.0%** |
| 4 | Qwen2-1.5B | 1.5B | 56.5% |
| 5 | Phi-2 | 2.5B | 52.7% |
| 6 | Llama-2-7B | 7B | 45.3% |
| 7 | Gemma-2B | 2B | 42.3% |
| 8 | Llama-1-7B | 7B | 35.1% |

**Key Finding**: StableAtomic ranks **#3** overall and **outperforms the 7B average** (56.4%) by **+3.6%**.

### Parameter Efficiency

| Model | Params | MMLU | Efficiency (MMLU/B) |
|-------|--------|------|---------------------|
| **StableAtomic** | **2.3B** | **60.0%** | **26.1** |
| Qwen2-1.5B | 1.5B | 56.5% | 37.7 |
| Phi-2 | 2.5B | 52.7% | 21.1 |
| Llama-2-7B | 7B | 45.3% | 6.5 |
| Mistral-7B | 7B | 71.6% | 10.2 |

**Key Finding**: StableAtomic achieves Llama-2-7B level performance (45.3%) with **3x fewer parameters**.

---

## Comparison with Reasoning Models

### Leaderboard: Reasoning Models (MMLU)

| Rank | Model | Params | MMLU | Math |
|------|-------|--------|------|------|
| 1 | DeepSeek-R1 (MoE) | 671B | 90.8% | 97.3% |
| 2 | Qwen2.5-14B | 14B | 85.0% | 65.0% |
| 3 | Qwen2.5-Max | 30B | 76.1% | 76.1% |
| 4 | DeepSeek-R1-Distill-Qwen-32B | 32B | 72.6% | 83.3% |
| 5 | Mistral-7B | 7B | 71.6% | 28.2% |
| 6 | DeepSeek-R1-Distill-Qwen-14B | 14B | 69.7% | 80.0% |
| **7** | **StableAtomic** | **2.3B** | **60.0%** | **50.0%** |
| 8 | DeepSeek-R1-Distill-Qwen-7B | 7B | 55.5% | 83.3% |
| 9 | QwQ-32B-Preview | 32B | 50.0% | 60.0% |

### Key Insights

1. **Globular ranks #7** among reasoning-optimized models
2. **Not trained on reasoning**: Achieves 50% Math without explicit reasoning/COT training
3. **Vs DeepSeek-R1-Distill-7B**: StableAtomic leads in MMLU (+4.5%), trails in Math (-33.3%)
4. **Vs QwQ-32B**: StableAtomic leads in MMLU (+10.0%), competitive in Math

**Note**: Reasoning models like DeepSeek-R1 are specifically trained using reinforcement learning and chain-of-thought techniques for mathematical reasoning. Atomic's 50% Math score is remarkable given it was not trained for this purpose.

---

## Usage

### Loading the Model

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

model_path = "path/to/model"

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path, 
    trust_remote_code=True,
    torch_dtype=torch.float32
)
model.eval()
```

### Generation

```python
# Simple generation
messages = [{"role": "user", "content": "What is the capital of France?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
    outputs = model.generate(
        inputs.input_ids,
        max_new_tokens=256,
        temperature=0.7,
        do_sample=True
    )

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

### Chat Interface

```python
# Interactive chat
while True:
    user_input = input("You: ")
    if user_input.lower() in ['quit', 'exit']:
        break
    
    messages = [{"role": "user", "content": user_input}]
    # ... generation code ...
    print(f"Model: {response}\n")
```

---

## Model Configuration

Key parameters in `generation_config.json`:

```json
{
  "bos_token_id": 151643,
  "eos_token_id": [151645, 151643],
  "pad_token_id": 151643,
  "temperature": 0.7,
  "top_k": 20,
  "top_p": 0.8,
  "repetition_penalty": 1.1
}
```

---

## Comparison Charts

<!-- Add comparison charts here -->

### Benchmark Comparison (2B Models)
![Benchmark Comparison 2B](./images/benchmark_comparison.png)

### 7B Model Comparison  
![7B Comparison](./images/benchmark_7b_comparison.png)

### Reasoning Model Comparison
![Reasoning Comparison](./images/benchmark_reasoning_comparison.png)

---

## Technical Notes

1. **Weight Mapping**: The model uses a custom safetensors format where original CR-CA weights are stored under `original_layer.*` keys. These are automatically remapped during loading.

2. **Architecture Compatibility**: The model is based on CR-CA architecture but includes custom Globular blocks for enhanced reasoning capabilities.

3. **Memory Requirements**: 
   - FP32: ~9GB
   - FP16: ~4.5GB
   - INT8: ~2.3GB

---

## License

GNU Affero GPL v3.0

---

## Citation

If you use this model in your research, please cite:

```bibtex
@article{stableAtomic2026,
  title={Globular: Evolutionary Agent-Based Reasoning in Language Models},
  author={Euroswarms Institute},
  year={2026}
}
```

---

## Contact

For questions or issues, please open an issue on the repository.
Or, contact us via email at research@euroswarms.eu