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.gitattributes

@@ -13,3 +13,4 @@
 *.zip filter=lfs diff=lfs merge=lfs -texttokenizer.json filter=lfs diff=lfs merge=lfs -text
 3b-scu/tokenizer.json filter=lfs diff=lfs merge=lfs -text
 3b-fixed/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,379 +1,206 @@
 ---
-license: 
-- apache-2.0
-- llama3.2
-tags:
-- llama
-- control-theory
-- regularization
-- automatic-control
-- information-theory
-language:
-- en
 base_model: meta-llama/Llama-3.2-1B
-library_name: transformers
+library_name: peft
+tags:
+- base_model:adapter:meta-llama/Llama-3.2-1B
+- lora
+- transformers
 ---
 
-# Shannon Control Unit
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **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. -->
 
-*Automatic rate-distortion control for neural network training*
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
 
-[![Model](https://img.shields.io/badge/🤗_Hugging_Face-Models-yellow)](https://huggingface.co/hunterbown/shannon-control-unit)
-[![Parameters](https://img.shields.io/badge/Parameters-1B_|_3B_|_8B-blue)]()
-[![Control](https://img.shields.io/badge/Control_Precision-±0.12pp-green)]()
+## Uses
 
-## Key Innovation
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-Automatic closed-loop control system for neural network regularization based on the Minimum Description Length (MDL) principle. The system manages an interpretable "information budget" - the proportion of total description length allocated to model complexity.
+### Direct Use
 
-### Theoretical Foundation
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-The loss function implements the MDL two-part code:
-```
-L_total = L_data + λ(t)·L_params
-```
-
-Where:
-- **L_data** (DataBPT): Cross-entropy loss in bits - the cost to encode data given the model
-- **L_params** (ParamBPT): Negative log prior of weights under N(0,σ²I) in bits - the cost to encode the model itself
-- **λ(t)**: Dynamically adjusted Lagrange multiplier (from rate-distortion theory)
-
-The controlled variable S represents the actual information allocation ratio (the control target):
-```
-S = L_params / (L_data + L_params)
-```
+[More Information Needed]
 
-This answers: "What percentage of the total information cost describes the model?"
-(Note: S is independent of the regularization weight λ)
+### Downstream Use [optional]
 
-## Control Dynamics
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-<!-- Control dynamics visualization will be added here -->
+[More Information Needed]
 
-*The 3B model demonstrates robust control: starting far above target, the PI controller automatically adjusts λ from 0.5 to 2.6, achieving stable control at 2.88% (close to target: 3.0%). This showcases excellent convergence - a key property of robust control systems.*
+### Out-of-Scope Use
 
-## Performance at 250 Steps
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-### 1B Models
-| Model | Data BPT | Param BPT | Total BPT | Share S% | λ Value | Control |
-|-------|----------|-----------|-----------|----------|---------|---------|
-| **Baseline (CE)** | 3.315 | - | 3.315 | 0.00% | - | None |
-| **Fixed (λ=0.5)** | 4.325 | 0.110 | 4.435 | 2.48% | 0.5 | Manual |
-| **SCU** | 3.771 | 0.114 | 3.885 | **2.93%** | 1.000 | ✓ Auto |
-
-### 3B Models  
-| Model | Data BPT | Param BPT | Total BPT | Share S% | λ Value | Control |
-|-------|----------|-----------|-----------|----------|---------|---------|
-| **Baseline (CE)** | 4.092 | - | 4.092 | 0.00% | - | None |
-| **Fixed (λ=0.5)** | 2.773 | 0.096 | **2.869** | 3.35% | 0.5 | Manual |
-| **SCU** | 3.236 | 0.096 | 3.332 | **2.88%** | 2.607 | ✓ Auto |
-
-*Note: Models intentionally undertrained (250 steps) to demonstrate control robustness in compute-constrained settings. The 3B baseline's higher BPT than 1B (4.092 vs 3.315) reflects insufficient training data for the larger model's parameter count.*
-
-## Quick Start
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import PeftModel
-import torch
-
-# For 3B models (LoRA adapters in subfolders)
-base_model = AutoModelForCausalLM.from_pretrained(
-    "meta-llama/Llama-3.2-3B",
-    torch_dtype=torch.float16,
-    device_map="auto"
-)
-# Load SCU version (or use subfolder="3b-fixed" for fixed lambda)
-model = PeftModel.from_pretrained(
-    base_model, 
-    "hunterbown/shannon-control-unit",
-    subfolder="3b-scu"
-)
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B")
-
-# Generate text
-inputs = tokenizer("The future of AI is", return_tensors="pt")
-outputs = model.generate(**inputs, max_length=50)
-print(tokenizer.decode(outputs[0]))
-```
-
-## Model Specifications
-
-<table>
-<tr>
-<td>
-
-### 1B Model
-- **Base**: Llama-3.2-1B
-- **Parameters**: 1.24B
-- **Training**: 512K tokens
-- **Control λ**: 1.000
-- **Target S**: 3.0%
-- **Achieved S**: 2.93%
-
-</td>
-<td>
-
-### 3B Model  
-- **Base**: Llama-3.2-3B
-- **Parameters**: 3.21B
-- **Training**: 512K tokens
-- **Control λ**: 2.607
-- **Target S**: 3.0%
-- **Achieved S**: 2.88%
-
-</td>
-<td>
-
-### 8B Model (Planned)
-- **Base**: Apertus-8B
-- **Parameters**: 8B
-- **Training**: 2M tokens
-- **Expected λ**: ~3-5
-- **Target S**: 1.0%
-- **Status**: Q4 2025
-
-</td>
-</tr>
-</table>
-
-## Technical Details
-
-### Formal Definitions
-
-**Bits Per Token (BPT)** - Information-theoretic measurements:
-- **DataBPT**: Cross-entropy loss using base-2 logarithm: `-1/N Σ log₂ P(token_i | context_i)`
-- **ParamBPT**: Negative log prior of LoRA weights under N(0,σ²I), normalized by tokens N and converted to bits (nats / ln 2). In practice, this is equivalent to an L2 penalty.
-- **Prior**: Isotropic Gaussian with σ=0.01 (LoRA adapters initialized near zero)
-
-**Optimizer Configuration**: We use AdamW with `weight_decay=0` to avoid double regularization (since ParamBPT provides the regularization).
-
-### Shannon Control Unit Architecture
-
-```
-┌─────────────┐     Error      ┌─────────────┐
-│ Current S% ├───────────────> │ PI Control  │
-└─────────────┘        │        └──────┬──────┘
-       ↑               │               │
-       │               ↓               ↓
-┌──────┴──────┐  ┌──────────┐  ┌──────────┐
-│ Measure S%  │  │ Target S │  │ Update λ │
-└──────┬──────┘  └──────────┘  └─────┬────┘
-       ↑                              │
-       │         ┌──────────┐         │
-       └─────────┤   Model  │<────────┘
-                 └──────────┘
-```
-
-### Control Algorithm
-
-```python
-# PI controller with deadband, positivity guarantee, and anti-windup
-def update_lambda(lmbda, S, S_target, I, Kp=1.2, Ki=0.25, 
-                  deadband=0.003, I_min=-0.1, I_max=0.1, 
-                  lmbda_min=1e-4, lmbda_max=50.0):
-    """Robust PI controller ensuring λ > 0"""
-    error = S_target - S
-    
-    # Deadband - only update if outside tolerance
-    if abs(error) <= deadband:
-        return lmbda, I
-    
-    # Anti-windup: clamp integral term
-    I = max(I_min, min(I_max, I + error))
-    
-    # Multiplicative update guarantees λ > 0
-    lmbda = lmbda * math.exp(Kp*error + Ki*I)
-    
-    # Final safety clamps
-    lmbda = max(lmbda_min, min(lmbda, lmbda_max))
-    
-    return lmbda, I
-```
-
-### Loss Function
-
-```
-L_total = L_data + λ(t)·L_params
-
-where:
-- L_data: Cross-entropy loss (bits)
-- L_params: KL(weights || prior)
-- λ(t): Automatically controlled multiplier
-```
-
-## Scaling Behavior
-
-| Model Size | Optimal λ | Target S% | Training Data | Status |
-|------------|-----------|-----------|---------------|---------|
-| 1B | 1.000 | 3.25% | 512K tokens | ✅ Complete |
-| 3B | 2.607 | 3.00% | 512K tokens | ✅ Complete |
-| 8B | ~3-5 | 1.00% | 2M tokens | 📅 Q4 2025 |
-| 70B | ~10-15 | 0.50% | 10M tokens | 🔬 Research |
-
-*Training data scales with model size to maintain consistent data/parameter ratios.*
-
-## Significance
-
-### Why Automatic Control Matters
-
-**The Problem**: Traditional fixed regularization (λ=0.5) gives you whatever S% it produces - you have no control over the information allocation. As seen in our results, fixed λ=0.5 yields:
-- 1B: S=2.48% (missed target by 17%)
-- 3B: S=3.35% (missed target by 12%)
-
-**The Solution**: SCU automatically finds and maintains your exact target:
-- 1B: Achieved 2.93% (target 3.0%) - Error: 0.07pp
-- 3B: Achieved 2.88% (target 3.0%) - Error: 0.12pp
-
-### Key Advantages
-
-1. **Precise Control**: Maintains parameter share within ±0.12pp of any target
-2. **Automatic Adaptation**: No manual hyperparameter search needed
-3. **Scale-Invariant**: Different model sizes automatically find appropriate λ values
-4. **Real-Time Monitoring**: Watch information allocation during training
-5. **Robustness**: Works even with limited training data (demonstrated at 250 steps)
-
-### Understanding the "Anomalous" Results
-
-The experimental results that might appear counterintuitive actually provide strong evidence for adaptive control:
-
-**1B Fixed (λ=0.5) showing 4.435 BPT vs baseline 3.315:**
-- Fixed regularization from step 0 severely impedes initial learning
-- The model can't effectively fit data within 250 steps under heavy constraint
-- This demonstrates why adaptive control is necessary - optimal λ changes during training
-
-**3B baseline worse than 1B (4.092 vs 3.315 BPT):**
-- Larger model + insufficient data (512K tokens) = undertraining
-- 3B has excess capacity, begins memorizing noise
-- SCU correctly responds: finds λ=2.607 (vs 1.000 for 1B) to constrain the excess capacity
-
-These results collectively argue against "one-size-fits-all" regularization and demonstrate SCU's value in automatically finding appropriate λ for each situation.
+[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]
 
 ## Training Details
 
-<details>
-<summary><b>Configuration & Reproducibility</b></summary>
+### Training Data
 
-```yaml
-# Model Configuration
-base_model: Llama-3.2
-lora_r: 16
-lora_alpha: 16
-learning_rate: 1.67e-5
-batch_size: 16
-gradient_accumulation: 4
+<!-- 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. -->
 
-# PI Controller Parameters (Critical for Reproducibility)
-Kp: 2.0           # Proportional gain
-Ki: 0.5           # Integral gain  
-deadband: 0.005   # ±0.5% tolerance before control action
-ema_alpha: 0.95   # Exponential moving average smoothing
+[More Information Needed]
 
-# Information Budget Targets
-1B_model:
-  target_S: 0.0325  # 3.25%
-  initial_λ: 1.0
-  converged_λ: 1.000
-  
-3B_model:
-  target_S: 0.03    # 3.0%
-  initial_λ: 1.0
-  converged_λ: 2.607
+### Training Procedure
 
-# Prior Distribution for KL Term
-prior_type: "Gaussian"
-prior_sigma: 0.01  # N(0, 0.01²I) for LoRA adapters
-```
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-</details>
+#### Preprocessing [optional]
 
-<details>
-<summary><b>Reproducibility</b></summary>
+[More Information Needed]
 
-Full training code and reproducibility instructions available upon request. The method uses standard PyTorch and HuggingFace Transformers libraries.
 
-</details>
+#### Training Hyperparameters
 
-## Background
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
-> "Information is the resolution of uncertainty." - Claude Shannon, Bell Labs, 1948
+#### Speeds, Sizes, Times [optional]
 
-The Shannon Control Unit applies Shannon's information theory to neural network training, treating parameter capacity as a communication channel with limited bandwidth.
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
-## Related Work
+[More Information Needed]
 
-### Foundational Theory
-- **Rate-Distortion Theory** (Shannon, 1959): Minimizing distortion subject to rate constraints using Lagrange multipliers
-- **MDL Principle** (Hinton & van Camp, 1993): Neural networks as two-part codes (model + data|model)
-- **Modern MDL** (Blier & Ollivier, 2018): Large NNs as effective data compressors
+## Evaluation
 
-### Control in ML
-- **ControlVAE** (Shao et al., 2020): PI control for KL divergence in VAEs - direct inspiration for our approach
-- **PPO/RLHF**: Adaptive KL penalties to constrain policy updates within trust regions
-- **Adaptive Regularization**: Various scheduling methods (learning rate, dropout, weight decay)
+<!-- This section describes the evaluation protocols and provides the results. -->
 
-### Our Contribution
-While components exist separately, SCU uniquely combines:
-1. MDL-based information budget (S) as control objective
-2. Formal PI control for automatic λ adjustment  
-3. Application to LLM training with proven ±0.01% precision
+### Testing Data, Factors & Metrics
 
-### Comparative Analysis
+#### Testing Data
 
-| Method | Control Objective | Mechanism | Domain |
-|--------|------------------|-----------|---------|
-| ControlVAE | Fix KL divergence at C | PI control on β | VAEs |
-| PPO w/ Adaptive KL | Constrain policy KL < δ | Proportional control | RL |
-| MDL (Hinton '93) | Minimize L(D,W) | Gradient descent | NNs |
-| **SCU (Ours)** | **Fix info budget S%** | **PI control on λ** | **LLMs** |
+<!-- This should link to a Dataset Card if possible. -->
 
-## Frequently Asked Questions
+[More Information Needed]
 
-**Q: Why is this better than fixed λ?**
-A: Fixed λ gives you whatever S% it happens to produce. SCU gives you exactly the S% you want, automatically.
+#### Factors
 
-**Q: Why would I want a specific S%?**
-A: Different S% values may be optimal for different objectives:
-- Lower S% (1-2%): Stronger regularization, more compression
-- Medium S% (3-5%): Balanced capacity
-- Higher S% (≥5%): Weaker regularization, risk of overfit
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 
-**Q: Is 250 steps enough to prove this works?**
-A: For demonstrating control precision, yes. The PI controller converges and maintains target within 50-100 steps. Extended training would show performance implications.
+[More Information Needed]
 
-**Q: Why does 3B baseline have worse BPT than 1B?**
-A: Insufficient training data for the larger model (512K tokens). This actually demonstrates SCU's robustness - it maintains precise control even when models are struggling.
+#### Metrics
 
-## Citation
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
-If you use this method in your research, please cite:
+[More Information Needed]
 
-```bibtex
-@misc{bown2025scu,
-  title={Shannon Control Unit: Automatic Rate-Distortion Control for Neural Network Training},
-  author={Bown, Hunter},
-  year={2025},
-  url={https://huggingface.co/hunterbown/shannon-control-unit},
-  note={HuggingFace Model Repository}
-}
-```
+### Results
 
-## Links
+[More Information Needed]
 
-- **Model Repository**: This HuggingFace page
-- **Contact**: Via HuggingFace discussions
+#### Summary
 
-## License
 
-Model weights: Llama 3.2 Community License  
-Training method & SCU implementation: Apache-2.0 License (open source)
 
----
+## 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]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
 
-<div align="center">
+[More Information Needed]
 
-**Built with information theory at its core**
+## Model Card Contact
 
-*A tribute to Claude Shannon and Bell Labs' pioneering work in information theory*
+[More Information Needed]
+### Framework versions
 
-</div>
+- PEFT 0.17.1

adapter_config.json

@@ -1,6 +1,9 @@
 {
   "alpha_pattern": {},
-  "auto_mapping": null,
+  "auto_mapping": {
+    "base_model_class": "LlamaForCausalLM",
+    "parent_library": "transformers.models.llama.modeling_llama"
+  },
   "base_model_name_or_path": "meta-llama/Llama-3.2-1B",
   "bias": "none",
   "corda_config": null,
@@ -13,9 +16,9 @@
   "layers_pattern": null,
   "layers_to_transform": null,
   "loftq_config": {},
-  "lora_alpha": 16,
+  "lora_alpha": 32,
   "lora_bias": false,
-  "lora_dropout": 0.05,
+  "lora_dropout": 0.1,
   "megatron_config": null,
   "megatron_core": "megatron.core",
   "modules_to_save": null,
@@ -25,16 +28,13 @@
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
-    "up_proj",
-    "gate_proj",
-    "down_proj",
-    "v_proj",
     "q_proj",
+    "v_proj",
     "k_proj",
     "o_proj"
   ],
   "target_parameters": null,
-  "task_type": "CAUSAL_LM",
+  "task_type": null,
   "trainable_token_indices": null,
   "use_dora": false,
   "use_qalora": false,

adapter_model.safetensors

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+oid sha256:8480f5beca107008394abc5d9237129fda74eac4f6823759edee6144a30b0aa0
+size 13648488

ce_kl_auto_metrics.jsonl

@@ -0,0 +1,27 @@
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config.json

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+{
+  "model_name": "meta-llama/Llama-3.2-1B",
+  "output_dir": "models/scu_fixed_sigma_20250903_222442",
+  "mode": "ce_kl_auto",
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+  "max_epochs": 10,
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+  "batch_size": 1,
+  "gradient_accumulation_steps": 4,
+  "learning_rate": 0.0005,
+  "fp16": true,
+  "data_path": "training_data/train_512k.txt",
+  "sample_size": 2000000,
+  "lora_r": 16,
+  "lora_alpha": 32,
+  "prior_sigma": 0.1,
+  "target_S": 0.01,
+  "lam_init": 1.0,
+  "l2_weight": 0.1
+}

mdl_ledger.jsonl

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+{"timestamp": 1756956507.313285, "tokens_seen": 274432, "data_bpt": 2.098, "param_bpt": 0.034, "S": 0.0159, "lambda": 0.420749, "log_lambda": -0.8657, "integral": 0.0455, "prev_hash": "0989f27ffd0f60c8264e460f30cd3b24c9c3c8617b436df6093a9ee50a094f02", "hash": "ca600d4978bdbd73d0bfb3a82662468d31213c79b9954a0d64ba110fff0d7590"}
+{"timestamp": 1756956508.1355221, "tokens_seen": 275456, "data_bpt": 2.1048, "param_bpt": 0.0341, "S": 0.0159, "lambda": 0.462824, "log_lambda": -0.7704, "integral": 0.0483, "prev_hash": "ca600d4978bdbd73d0bfb3a82662468d31213c79b9954a0d64ba110fff0d7590", "hash": "0d008128758f14a8927df76c0b903be9399ffb79cb0691fa456298752faa7d9c"}

training_summary.json

@@ -0,0 +1,19 @@
+{
+  "mode": "ce_kl_auto",
+  "model": "meta-llama/Llama-3.2-1B",
+  "steps": 270,
+  "N_tokens": 445718,
+  "target_S": 0.01,
+  "final_metrics": {
+    "data_bpt": 2.1664955498827014,
+    "param_bpt": 0.03528421491730657,
+    "total_bpt": 2.201779764800008,
+    "S_ratio": 0.016025315284206688,
+    "lambda": 0.46282422077910096
+  },
+  "avg_last_100": {
+    "data_bpt": 2.297084885769371,
+    "total_bpt": 2.3255800839060363,
+    "S_ratio": 0.01281555231171954
+  }
+}

validation_results.json

@@ -0,0 +1,16 @@
+{
+  "base": {
+    "bpt": 3.9199,
+    "perplexity": 15.14
+  },
+  "scu_trained": {
+    "bpt": 3.6761,
+    "perplexity": 12.78,
+    "adapter_path": "models/scu_fixed_sigma_20250903_222442"
+  },
+  "improvement": {
+    "delta_bpt": 0.2438,
+    "percent": 6.22,
+    "better": true
+  }
+}