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README.md

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- rag
+- faithfulness
+- hallucination-detection
+- lora
+- microguard
+datasets:
+- galileo-ai/ragbench
+- wandb/RAGTruth-processed
+- PatronusAI/HaluBench
+metrics:
+- balanced_accuracy
+- f1
+pipeline_tag: text-classification
+base_model: HuggingFaceTB/SmolLM2-135M-Instruct
+---
+
+# MicroGuard — SmolLM-135M
+
+A LoRA-adapted faithfulness classifier for RAG systems. Detects whether a generated answer is faithful to the retrieved context.
+
+## Performance
+
+| Metric | Value |
+|--------|-------|
+| Balanced Accuracy | 64.3% |
+| F1 Score | 0.661 |
+| Cohen's Kappa | 0.329 |
+| Inference Latency | 72ms |
+
+Evaluated on a combined test set of 15,976 examples from RAGBench, RAGTruth, and HaluBench.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+base = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM2-135M-Instruct")
+model = PeftModel.from_pretrained(base, "tarun5986/MicroGuard-SmolLM-135M")
+tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-135M-Instruct")
+
+# Or use the MicroGuard package
+from microguard import MicroGuard
+guard = MicroGuard(model="tarun5986/MicroGuard-SmolLM-135M", base_model="HuggingFaceTB/SmolLM2-135M-Instruct")
+result = guard.check(
+    context="The Eiffel Tower was built in 1889 by Gustave Eiffel.",
+    question="Who built the Eiffel Tower?",
+    answer="The Eiffel Tower was built by Gustave Eiffel in 1889."
+)
+print(result)  # {'verdict': 'FAITHFUL', 'confidence': 74.2, 'latency_ms': 64.0}
+```
+
+## Training
+
+- **Method**: LoRA (r=16, alpha=32, targets: q,k,v,o projections)
+- **Data**: 127,932 examples from RAGBench + RAGTruth + HaluBench
+- **Evaluation**: Constrained decoding via logit comparison (0% garbage outputs)
+
+## Paper
+
+[MicroGuard: Sub-Billion Parameter Faithfulness Classification for Real-Time RAG QA](https://github.com/tarun-ks/MicroGuard)
+
+## Citation
+
+```bibtex
+@article{microguard2026,
+  title={MicroGuard: Sub-Billion Parameter Faithfulness Classification for Real-Time RAG QA},
+  author={Sharma, Tarun},
+  journal={IEEE Access},
+  year={2026}
+}
+```

adapter_config.json

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+{
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+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "HuggingFaceTB/SmolLM2-135M-Instruct",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.1",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "k_proj",
+    "o_proj",
+    "v_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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chat_template.jinja

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+{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
+You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>
+' }}{% endif %}{{'<|im_start|>' + message['role'] + '
+' + message['content'] + '<|im_end|>' + '
+'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
+' }}{% endif %}

special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>"
+  ],
+  "bos_token": {
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+    "lstrip": false,
+    "normalized": false,
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+    "single_word": false
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+    "lstrip": false,
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+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
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+    "<|im_start|>",
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+  "bos_token": "<|im_start|>",
+  "clean_up_tokenization_spaces": false,
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+  "extra_special_tokens": {},
+  "model_max_length": 8192,
+  "pad_token": "<|im_end|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>",
+  "vocab_size": 49152
+}

training_args.bin

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