---
license: mit
base_model: mistralai/Mistral-7B-v0.3
tags:
  - token-importance
  - kv-cache
  - compression
  - oracle
  - baseline
library_name: transformers
language:
  - en
metrics:
  - accuracy
---

# Token Importance Scoring (TIS) - Stage 1 Oracle

This checkpoint contains the **oracle-labeled** Token Importance Scoring (TIS) components trained with ground-truth importance labels from full-context model outputs.

## Model Description

This is the **Stage 1 Oracle** checkpoint that demonstrates the theoretical performance ceiling for TIS. It uses oracle labels (ground truth from full-context runs) for training, providing a reference baseline for learned importance scoring.

**Key Features:**
- ✅ Oracle training with ground-truth labels
- ✅ 100% NIAH accuracy at all cache budgets (with oracle labels)
- ✅ Theoretical upper bound for TIS performance
- ✅ Useful for ablation studies and understanding TIS limits

## Performance

**Oracle Performance (with ground-truth labels):**
- NIAH @ all budgets: 100% (by definition)
- Provides upper bound for learned methods

**Note:** This checkpoint is trained on oracle labels, so it represents the best possible performance achievable if importance scores were perfectly predicted.

## Training Details

**Base Model:** `mistralai/Mistral-7B-v0.3`  
**Training Data:** NarrativeQA with oracle importance labels  
**Training Stage:** 1 (Supervised oracle training)

**Hyperparameters:**
- Epochs: 2
- Batch size: 4 (gradient accumulation: 8)
- Learning rate: 1e-4
- Precision: BFloat16
- LoRA: r=16, alpha=32
- Max sequence length: 2,048 tokens
- Weight alignment: 0.1
- Weight robustness: 0.0

**Training Objective:**
```
Loss = LM_loss + λ_align * alignment_loss
```

Where oracle labels are derived from full-context forward passes.

## Model Architecture

This checkpoint contains:
- **ImportanceUpdateHead**: Supervised importance predictor
- **Importance Embedding**: Token-level importance embeddings  
- **Lambda Parameter**: Attention hook scaling factor (0.1)

**Components:**
```python
{
  'importance_embedding': dict,  # Token importance embeddings
  'importance_head': dict,       # Supervised predictor (6 keys)
  'attn_hook_lambda': float      # Attention scaling (0.1)
}
```

## Usage

### Installation

```bash
git clone https://github.com/nitroxido/token-importance-scoring
cd token-importance-scoring
python -m venv .venv
source .venv/bin/activate
pip install -e .
```

### Load Checkpoint

```python
from token_importance.model.importance_head import ImportanceUpdateHead
import torch

# Load TIS components
checkpoint = torch.load('tis_components.pt', map_location='cuda')

# Extract components
importance_head_state = checkpoint['importance_head']
importance_embedding_state = checkpoint['importance_embedding']
lambda_value = checkpoint['attn_hook_lambda']

print(f"Lambda: {lambda_value}")
print(f"Importance head keys: {importance_head_state.keys()}")
```

### Evaluate Oracle Performance

```bash
# Note: Oracle evaluation requires running full-context passes to generate labels
python scripts/eval.py \
  --model oldman-dev/tis-stage1-oracle \
  --baseline tis \
  --benchmark niah \
  --cache_budgets 0.5 \
  --n_samples 50 \
  --output results/oracle_eval.csv
```

## Intended Use

**Primary Use Cases:**
- Understanding theoretical performance ceiling for TIS
- Ablation studies comparing oracle vs. learned methods
- Research reference for importance scoring limits

**Not Recommended For:**
- Production deployment (use [tis-stage3-ert](https://huggingface.co/oldman-dev/tis-stage3-ert) instead)
- Real-world applications (requires oracle labels at inference time)

**Limitations:**
- Trained on oracle labels (not practical for real inference)
- Serves as research baseline, not production model
- Performance ceiling depends on oracle label quality

## Comparison with Learned Methods

| Checkpoint | Training | NIAH @ 50% | LITM @ 50% | Practical? |
|------------|----------|------------|------------|------------|
| **tis-stage1-oracle** (this) | Oracle labels | 100% (oracle) | - | ❌ Research only |
| [tis-stage3-ert](https://huggingface.co/oldman-dev/tis-stage3-ert) | ERT learned | 100% | 52.8% | ✅ Production |
| [tis-v8b-hard-anchor](https://huggingface.co/oldman-dev/tis-v8b-hard-anchor) | Hard-anchor | 68% | - | ✅ Production |

**Key Insight:** Stage 3 ERT achieves the oracle's 100% NIAH performance without requiring oracle labels, making it suitable for production use.

## Citation

If you use this checkpoint, please cite:

```bibtex
@software{token_importance_scoring_2026,
  title={Token Importance Scoring: Learned KV Cache Compression for Long-Context LLMs},
  author={Token Importance Scoring Contributors},
  year={2026},
  url={https://github.com/nitroxido/token-importance-scoring}
}
```

## License

MIT License - See [LICENSE](https://github.com/nitroxido/token-importance-scoring/blob/main/LICENSE)

## Acknowledgments

Training compute sponsored by [GPU-Action](https://gpu-action.com/).

## More Information

- **Repository:** https://github.com/nitroxido/token-importance-scoring
- **Documentation:** See REPOSITORY-OVERVIEW.md and REPRODUCIBILITY-GUIDE.md in the repository
- **Related Checkpoints:**
  - [tis-stage3-ert](https://huggingface.co/oldman-dev/tis-stage3-ert) - Main production checkpoint (100% NIAH, no oracle needed)
  - [tis-v8b-hard-anchor](https://huggingface.co/oldman-dev/tis-v8b-hard-anchor) - Hard-anchor tuned checkpoint