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+# FunctionGemma Infrastructure LiteRT-LM
+
+A fine-tuned [FunctionGemma-270M](https://huggingface.co/google/functiongemma-270m-it) model converted to LiteRT-LM format for on-device inference. Designed for self-healing infrastructure and automatic error remediation.
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| Base Model | google/functiongemma-270m-it |
+| Format | LiteRT-LM (.litertlm) |
+| Quantization | Dynamic INT8 |
+| File Size | 272 MB |
+| Parameters | 270M |
+
+## Intended Use
+
+This model is designed for [dad-express](https://github.com/anthropics/dad-express), a self-healing gateway that monitors HTTP traffic and automatically fixes infrastructure configuration issues by calling the appropriate tools.
+
+## Supported Tools
+
+The model was fine-tuned on 9 infrastructure tools:
+
+| Tool | Description | Parameters |
+|------|-------------|------------|
+| `addProxyRoute` | Add reverse proxy route | path, upstream, port |
+| `addCorsHeaders` | Configure CORS headers | origin, credentials |
+| `configureSsl` | Configure SSL certificate | hostname, selfSigned |
+| `setEnvVariable` | Set environment variable | name, value |
+| `exposePort` | Expose port in Docker/firewall | service, port |
+| `addHostEntry` | Add hostname to /etc/hosts | hostname, ip |
+| `restartService` | Restart a service | service |
+| `clearCache` | Clear cache | cacheType |
+| `modifyConfig` | Modify config file | file, key, value |
+
+## Training Details
+
+### Dataset
+- **Total Examples**: 10,216
+- **Train/Eval Split**: 90/10 (9,194 train, 1,022 eval)
+- **Format**: Prompt-completion pairs using FunctionGemma chat template
+- **Distribution**: ~1,000-1,200 examples per tool (balanced)
+
+### Training Configuration
+
+Trained following [Google's official FunctionGemma fine-tuning notebook](https://colab.research.google.com/github/google-gemini/gemma-cookbook/blob/main/FunctionGemma/%5BFunctionGemma%5DFinetune_FunctionGemma_270M_for_Mobile_Actions_with_Hugging_Face.ipynb):
+
+```python
+SFTConfig(
+    num_train_epochs=2,
+    per_device_train_batch_size=4,
+    gradient_accumulation_steps=8,
+    learning_rate=1e-5,
+    lr_scheduler_type="cosine",
+    gradient_checkpointing=True,
+    packing=False,
+    optim="adamw_torch_fused",
+    bf16=True,
+    completion_only_loss=True,  # Critical: only train on completion tokens
+)
+```
+
+### Hardware
+- **GPU**: NVIDIA L4 (24GB)
+- **Training Time**: ~23 minutes
+- **Conversion Time**: ~10 minutes
+
+### Performance Metrics
+
+| Metric | Value |
+|--------|-------|
+| Final Eval Loss | 0.034 |
+| Token Accuracy | 98.6% |
+| Training Steps | 576 |
+
+## LiteRT-LM Conversion
+
+Converted using [ai-edge-torch](https://github.com/google-ai-edge/ai-edge-torch):
+
+```python
+converter.convert_to_litert(
+    pytorch_model,
+    prefill_seq_len=256,
+    kv_cache_max_len=1024,
+    quantize="dynamic_int8",
+    output_format="litertlm",
+)
+```
+
+### LLM Metadata
+```protobuf
+start_token: { token_ids: { ids: [ 2 ] } }
+stop_tokens: { token_str: "<end_of_turn>" }
+stop_tokens: { token_str: "<start_function_response>" }
+llm_model_type: { function_gemma: {} }
+```
+
+## Usage
+
+### With LiteRT-LM Runtime
+```typescript
+import { LiteRTLM } from 'litert-lm';
+
+const model = await LiteRTLM.load('functiongemma-infrastructure_q8_ekv1024.litertlm');
+const response = await model.generate(prompt);
+```
+
+### Example Input/Output
+
+**Input:**
+```
+Error: CORS - No 'Access-Control-Allow-Origin' header from http://localhost:3000
+```
+
+**Output:**
+```
+<start_function_call>call:addCorsHeaders{origin:<escape>http://localhost:3000<escape>,credentials:<escape>true<escape>}<end_function_call><start_function_response>
+```
+
+## Dependencies
+
+- Python 3.11+
+- transformers==4.57.1
+- trl==0.25.1
+- datasets==4.4.1
+- ai-edge-torch-nightly
+- ai-edge-litert-nightly
+
+## License
+
+This model inherits the [Gemma Terms of Use](https://ai.google.dev/gemma/terms).
+
+## Citation
+
+```bibtex
+@misc{functiongemma-infrastructure-litertlm,
+  title={FunctionGemma Infrastructure LiteRT-LM},
+  author={dad-express contributors},
+  year={2024},
+  publisher={Hugging Face},
+  url={https://huggingface.co/macmacmacmac/functiongemma-infrastructure-litertlm}
+}
+```