Add model card: 70M distilled model, 7.4/10 quality, 13ms ONNX

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- token-compression
+- context-optimization
+- llm
+- agentic
+- modernbert
+- distilled
+metrics:
+- f1
+- accuracy
+pipeline_tag: token-classification
+model-index:
+- name: kompress-small
+  results:
+  - task:
+      type: token-classification
+      name: Token Compression
+    metrics:
+    - name: Quality Score (Claude-judged)
+      type: custom
+      value: 7.4
+    - name: Latency ONNX (median)
+      type: latency
+      value: 13ms
+---
+
+# Kompress-Small: Distilled Token Compressor (70M params, 13ms ONNX)
+
+**6-layer distilled version of [kompress-base](https://huggingface.co/chopratejas/kompress-base).** Same quality, 4x faster, half the size.
+
+Compresses text in LLM context windows so agents can do more with less. Distilled from the 22-layer teacher using knowledge distillation — the student learns the teacher's probability distributions, not just hard labels.
+
+## Results
+
+| Model | Quality | Latency | Size | Params |
+|-------|---------|---------|------|--------|
+| **kompress-small (ONNX)** | **7.4/10** | **13-29ms** | **279MB** | **70M** |
+| kompress-base | 7.9/10 | 49-84ms | 600MB | 150M |
+| LLMLingua-2 | 5.9-6.2/10 | 113-117ms | 710MB | 179M |
+
+### Detailed Benchmarks
+
+**Unstructured text** (assistant explanations, plans, error analysis, status summaries):
+
+| Metric | kompress-small | kompress-base | LLMLingua-2 |
+|--------|---------------|--------------|-------------|
+| Quality (Claude-judged) | **7.4/10** | 7.9/10 | 5.9/10 |
+| Latency (median) | **29ms** | 84ms | 117ms |
+| Compression | 58% kept | 58% kept | 53% kept |
+
+**Real Claude Code sessions** (text segments):
+
+| Metric | kompress-small | kompress-base | LLMLingua-2 |
+|--------|---------------|--------------|-------------|
+| Quality (Claude-judged) | **7.4/10** | 7.3/10 | 6.2/10 |
+| Latency (median) | **13ms** | 83ms | 114ms |
+
+## Architecture
+
+Distilled from kompress-base by keeping 6 evenly-spaced layers from the original 22:
+
+```
+Teacher (kompress-base):  layers [0,1,2,...,21]  → 150M params, 600MB
+Student (kompress-small): layers [0,4,8,12,17,21] → 70M params, 279MB
+```
+
+Both token classification head and span importance CNN are copied from the teacher, then the full model is fine-tuned with knowledge distillation:
+
+```
+loss = 0.5 * CrossEntropy(student, labels) + 0.5 * KL_div(student, teacher) * T²
+```
+
+The soft KL divergence loss transfers richer information than hard labels alone — teaching the student *how confident* the teacher is about each token, not just the binary decision.
+
+### Available Formats
+
+| Format | Size | Latency | Use case |
+|--------|------|---------|----------|
+| `model.safetensors` | 279MB | ~77ms (MPS) | PyTorch inference |
+| `model_int8.pt` | 189MB | ~50ms (CPU) | PyTorch INT8 |
+| `model.onnx` + `.data` | 275MB | **13-29ms** (CPU) | Production deployment |
+
+## Usage
+
+### ONNX (Fastest)
+
+```python
+import onnxruntime as ort
+from transformers import AutoTokenizer
+
+sess = ort.InferenceSession("model.onnx")
+tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base")
+
+text = "Your text here..."
+words = text.split()
+enc = tokenizer(words, is_split_into_words=True, truncation=True,
+                max_length=8192, padding=True, return_tensors="np")
+
+logits = sess.run(None, {
+    "input_ids": enc["input_ids"],
+    "attention_mask": enc["attention_mask"]
+})[0][0]
+
+# Argmax: keep tokens where class 1 > class 0
+keep = logits[:, 1] > logits[:, 0]
+word_ids = enc.word_ids(batch_index=0)
+
+kept_words = []
+seen = set()
+for idx, wid in enumerate(word_ids):
+    if wid is not None and wid not in seen and keep[idx]:
+        kept_words.append(words[wid])
+        seen.add(wid)
+
+compressed = " ".join(kept_words)
+```
+
+### With Headroom
+
+```bash
+pip install headroom-ai
+```
+
+kompress-small is available as a lightweight alternative in [Headroom](https://github.com/headroom-ai/headroom).
+
+## Training Details
+
+- **Teacher**: kompress-base (22 layers, 150M params)
+- **Distillation**: 5 epochs, batch 32, lr 3e-5, temperature 4.0, α=0.5
+- **Data**: 263K examples (same extractive labels from 8 diverse datasets)
+- **Hardware**: NVIDIA H100
+- **Training time**: ~4 hours
+
+## Limitations
+
+- English only
+- Best on natural language text; use specialized compressors for JSON/code/logs
+- Sequences >512 tokens may degrade (inherited from ModernBERT layer pruning)
+
+## License
+
+Apache 2.0