Update README.md

README.md

@@ -1,199 +1,249 @@
 ---
+license: apache-2.0
+language:
+  - en
 library_name: transformers
-tags: []
+tags:
+  - text-generation
+  - causal-lm
+  - swarm-intelligence
+  - multi-agent
+  - pytorch
+  - transformers
+pipeline_tag: text-generation
+model-index:
+  - name: SAGI
+    results: []
 ---
 
-# Model Card for Model ID
+# SAGI - Swarm AGI Language Model
+
+SAGI is a novel causal language model that integrates **swarm intelligence dynamics** with transformer architecture. The model treats cognition as a dynamic, adaptive system where multiple internal "agents" collaborate through differentiable routing, trust mechanisms, and shared memory.
+
+## Model Description
+
+| Property | Value |
+|----------|-------|
+| Parameters | 52.72M |
+| Architecture | Transformer Decoder + Swarm Dynamics |
+| Hidden Size | 512 |
+| Layers | 6 |
+| Attention Heads | 8 |
+| Context Length | 2048 |
+| Vocabulary | GPT-2 tokenizer (50,257 tokens) |
+
+### Key Innovations
+
+- **Differentiable Routing**: Continuous mixture-of-experts via attention (`DiffRouter`) instead of hard module selection
+- **Adaptive Gating & Trust**: `MetaController` activates capacity under resource constraints; trust dynamics bias reliable components
+- **Episodic + Semantic Memory**: Dual memory system with trainable retrieval utility
+- **Curiosity Engine**: Injects novel goals when surprise is low, promoting exploration
+- **Self-Model & Rollback**: Predicts state transitions and detects anomalies for self-correction
+- **Resource Dynamics**: Soft conservation with learned converter; cognition consumes/recovers compute, memory, energy
+- **Value Monitoring**: Tracks alignment to core values and freezes plasticity under drift
+
+## How It Works
+
+```
+┌─────────────────────────────────────────────────────────┐
+│                       SAGI Model                         │
+├─────────────────────────────────────────────────────────┤
+│  ┌─────────────────┐      ┌─────────────────────────┐   │
+│  │   Swarm-7 V2.2  │─────▶│  Swarm State S, T       │   │
+│  │  (Cognitive     │      │  (Working Memory)       │   │
+│  │   Dynamics)     │      └───────────┬─────────────┘   │
+│  └────────▲────────┘                  │                 │
+│           │                           ▼                 │
+│           │              ┌─────────────────────────┐    │
+│           │              │  Transformer Decoder    │    │
+│           │              │  - Swarm-conditioned    │    │
+│           │              │    attention & FFN      │    │
+│           │              │  - RoPE embeddings      │    │
+│           │              └───────────┬─────────────┘    │
+│           │                          │                  │
+│  ┌────────┴────────┐      ┌─────────────────────────┐   │
+│  │   Observation   │◀─────│      LM Head            │   │
+│  │   (from tokens) │      └─────────────────────────┘   │
+│  └─────────────────┘                                    │
+└─────────────────────────────────────────────────────────┘
+```
+
+The swarm processes observations derived from token embeddings, updating its internal state **S**. This state conditions the transformer's attention patterns and feed-forward activations via learned projections, creating bidirectional information flow between symbolic (tokens) and subsymbolic (swarm dynamics) processing.
+
+## Usage
+
+### Installation
+
+```bash
+pip install torch transformers datasets
+```
+
+### Quick Start
+
+```python
+from transformers import AutoTokenizer
+from modeling_swarm import SwarmForCausalLM
+from configuration_swarm import SwarmConfig
+
+# Load model and tokenizer
+model = SwarmForCausalLM.from_pretrained("your-username/SAGI")
+tokenizer = AutoTokenizer.from_pretrained("your-username/SAGI")
+
+# Generate text
+model.eval()
+model.reset_swarm_state()  # Reset for clean generation
+
+prompt = "Once upon a time"
+inputs = tokenizer(prompt, return_tensors="pt")
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=100,
+    temperature=0.8,
+    top_k=50,
+    top_p=0.9,
+    do_sample=True,
+    pad_token_id=tokenizer.eos_token_id,
+)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### Inspecting Swarm State
+
+```python
+# Get current swarm state
+state = model.get_swarm_state()
+print(f"Active agents: {sum(state['active'][0])}")
+print(f"Goal stack depth: {state['goal_stack_size']}")
+
+# Reset swarm state between generations
+model.reset_swarm_state()
+```
+
+### Training
+
+```python
+from datasets import load_dataset
+from transformers import Trainer, TrainingArguments
+
+# Load dataset
+dataset = load_dataset("roneneldan/TinyStories", split="train[:5%]")
+
+# Tokenize
+def tokenize(examples):
+    return tokenizer(examples["text"], truncation=True, max_length=256)
+
+tokenized = dataset.map(tokenize, batched=True, remove_columns=["text"])
+
+# Train
+training_args = TrainingArguments(
+    output_dir="./sagi-finetuned",
+    per_device_train_batch_size=4,
+    learning_rate=3e-4,
+    num_train_epochs=1,
+    save_steps=500,
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized,
+)
+
+trainer.train()
+```
+
+## Model Architecture Details
+
+### Swarm Configuration
+
+| Parameter | Value | Description |
+|-----------|-------|-------------|
+| `max_agents` | 20 | Number of internal cognitive agents |
+| `dim_s` | 64 | State dimension |
+| `dim_t` | 32 | Task/goal dimension |
+| `dim_obs` | 48 | Observation dimension |
+| `topk_route` | 5 | Sparse routing top-k |
+| `K_thought_max` | 5 | Maximum thinking iterations per step |
 
-<!-- Provide a quick summary of what the model is/does. -->
+### Resource Budgets
 
+| Resource | Budget | Description |
+|----------|--------|-------------|
+| Compute | 60.0 | Compute budget per step |
+| Memory | 20.0 | Memory capacity |
+| Energy | 25.0 | Energy budget |
 
+### Trust & Plasticity
 
-## Model Details
+- **Trust Learning Rate**: 0.07
+- **Fast EMA (Plasticity)**: 0.10
+- **Slow EMA (Consolidation)**: 0.002
+- **Core Values**: `["truth", "safety", "efficiency"]`
+
+## Limitations
 
-### Model Description
+- **Early Research Model**: This is an experimental architecture exploring swarm-transformer integration
+- **Training Data**: Currently trained on TinyStories subset; may produce simple, story-like outputs
+- **Compute Requirements**: Swarm dynamics add overhead compared to standard transformers
+- **Generation Quality**: Model is undertrained; outputs may be repetitive or incoherent
 
-<!-- Provide a longer summary of what this model is. -->
+## Intended Use
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+This model is intended for:
+- Research into multi-agent cognitive architectures
+- Exploration of dynamic, adaptive language models
+- Educational purposes in understanding swarm intelligence + LLMs
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+Not intended for:
+- Production applications
+- Safety-critical systems
+- Generation of factual content
 
 ## Training Details
 
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
+- **Dataset**: TinyStories (subset)
+- **Optimizer**: AdamW (lr=3e-4, betas=(0.9, 0.999), weight_decay=0.01)
+- **Scheduler**: Cosine annealing
+- **Precision**: FP32
+- **Hardware**: CPU training (compatible with CUDA)
 
-**APA:**
+## Citation
 
-[More Information Needed]
+```bibtex
+@software{sagi2024,
+  title={SAGI: Swarm AGI Language Model},
+  author={},
+  year={2024},
+  url={https://huggingface.co/your-username/SAGI}
+}
+```
 
-## Glossary [optional]
+## License
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+Apache 2.0
 
-[More Information Needed]
+## Files
 
-## More Information [optional]
+- `config.json` - Model configuration
+- `model.safetensors` / `pytorch_model.bin` - Model weights
+- `tokenizer.json` - Tokenizer configuration
+- `modeling_swarm.py` - Model implementation (required for loading)
+- `configuration_swarm.py` - Config class (required for loading)
+- `model.py` - Swarm-7 V2.2 core system
 
-[More Information Needed]
+## Custom Code Notice
 
-## Model Card Authors [optional]
+This model requires custom code to run. When loading, use `trust_remote_code=True`:
 
-[More Information Needed]
+```python
+from transformers import AutoModelForCausalLM
 
-## Model Card Contact
+model = AutoModelForCausalLM.from_pretrained(
+    "your-username/SAGI",
+    trust_remote_code=True
+)
+```
 
-[More Information Needed]
+Or import the classes directly from the model files as shown in the Quick Start section.