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Model: oopere/SmolLM2-1.7B-ClinicalNER Source: Original Platform
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README.md
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---
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language:
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- en
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tags:
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- qlora
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- clinical-ner
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- smollm2
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- rearchitecting-llms
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- fine-tuning
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- medical
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- educational
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license: apache-2.0
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base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct
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datasets:
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- oopere/clinical-ner-qdora
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metrics:
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- accuracy
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---
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# SmolLM2-1.7B-ClinicalNER
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## Model Description
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QLoRA fine-tuned version of **HuggingFaceTB/SmolLM2-1.7B-Instruct** for clinical named entity
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recognition (NER). Created as part of **Chapter 7** of *Rearchitecting LLMs*.
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* **Book:** [Rearchitecting LLMs](https://hubs.la/Q04k2VyY0)
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* **Technique:** QLoRA (Quantized Low-Rank Adaptation)
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* **Task:** Clinical NER — structured JSON extraction from clinical notes
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* **Chapter:** Chapter 7 — Specialization Tuning
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[](https://hubs.la/Q040tvsK0)
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---
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## What This Model Does
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Given a free-text clinical note, the model extracts structured clinical entities
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into a strict JSON schema using only the two-word prompt `Extract:`.
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Before fine-tuning: a 15-line system prompt was required.
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After QLoRA training: the model responds correctly to `Extract:` alone.
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---
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## Schema Compliance Results
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Results from **CH07_NB02_L4_QLoRA_QDoRA** on the `oopere/clinical-ner-qdora` test set (40 samples, 5 categories).
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| Model | Prompt | Schema Compliance |
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|:---|:---|:---:|
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| SmolLM2-1.7B baseline | Strict (15-line prompt) | 87.5% |
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| SmolLM2-1.7B baseline | Minimal (`Extract:`) | 0.0% |
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| **SmolLM2-1.7B QLoRA (this model)** | **Minimal (`Extract:`)** | **95.0%** |
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Fine-tuning permanently absorbed the 15-line prompt into the model weights.
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---
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## Training Details
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### Dataset
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* **Source:** [oopere/clinical-ner-qdora](https://huggingface.co/datasets/oopere/clinical-ner-qdora)
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* **Train samples:** 200 (40 per category)
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* **Test samples:** 40 (8 per category)
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* **Categories:** clean, abbreviations, implicit, typos, irrelevant
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### QLoRA Hyperparameters
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| Parameter | Value |
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|:---|:---|
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| Base model | `HuggingFaceTB/SmolLM2-1.7B-Instruct` |
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| Quantization | NF4 4-bit + double quantization |
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| LoRA rank (r) | 8 |
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| LoRA alpha | 16 |
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| LoRA dropout | 0.05 |
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| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
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| Epochs | 3 |
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| Batch size | 8 |
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| Learning rate | 2e-4 |
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| LR scheduler | cosine |
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| Max sequence length | 512 |
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| Compute dtype | bfloat16 |
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### Hardware
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* **GPU:** NVIDIA L4 (Google Colab)
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---
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## How to Use
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = 'oopere/SmolLM2-1.7B-ClinicalNER'
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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model.eval()
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note = 'Patient 45yo male, fever and dry cough for 3 days. Temp 38.5C, HR 98, BP 120/80.'
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messages = [{'role': 'system', 'content': 'Extract:'}, {'role': 'user', 'content': note}]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer([text], return_tensors='pt').to(model.device)
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with torch.inference_mode():
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output_ids = model.generate(**inputs, max_new_tokens=256, do_sample=False)
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new_tokens = output_ids[0][len(inputs.input_ids[0]):]
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print(tokenizer.decode(new_tokens, skip_special_tokens=True))
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```
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---
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## Limitations & Intended Use
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**Educational model** from *Rearchitecting LLMs* Chapter 7.
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Demonstrates QLoRA fine-tuning and the workflow: Train → Merge → Upload → Verify.
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**Not intended for clinical or production use.** Training data is synthetic.
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---
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## Citation
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```bibtex
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@book{martra2026rearchitecting,
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author = {Pere Martra},
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title = {Rearchitecting LLMs: Structural techniques for efficient models},
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publisher = {Manning Publications},
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year = {2026},
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url = {https://hubs.la/Q040tvtp0}
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}
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```
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---
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## Acknowledgments
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Created following *Rearchitecting LLMs* (Manning, 2026).
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Challenge: can you push schema compliance above 95%?
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Try: higher LoRA rank, more epochs, or QDoRA instead.
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Share your results: [discussion forum](https://hubs.la/Q04k2VyY0)
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____
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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
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You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>
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' }}{% endif %}{{'<|im_start|>' + message['role'] + '
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' + message['content'] + '<|im_end|>' + '
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'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
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' }}{% endif %}
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"dtype": "bfloat16",
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"eos_token_id": 2,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 24,
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"rms_norm_eps": 1e-05,
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"rope_type": "default"
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"tie_word_embeddings": true,
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"dtype": "q4",
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"fp16": "float16",
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"model.onnx": true,
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"model_fp16.onnx": true
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}
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generation_config.json
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{
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"_from_model_config": true,
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"transformers_version": "5.0.0"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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"add_prefix_space": false,
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