Despina
/

Llama-3.2-3B-Instruct-re_gentune-2-shot

@@ -1,207 +1,152 @@
 ---
 base_model: meta-llama/Llama-3.2-3B-Instruct
 library_name: peft
 pipeline_tag: text-generation
 tags:
-- base_model:adapter:meta-llama/Llama-3.2-3B-Instruct
 - lora
-- transformers
 ---
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-### Framework versions
-- PEFT 0.18.1

 ---
+license: llama3.2
 base_model: meta-llama/Llama-3.2-3B-Instruct
 library_name: peft
+language:
+- en
 pipeline_tag: text-generation
 tags:
+- relation-extraction
+- information-extraction
+- qlora
 - lora
+- peft
+- llama
+- nlp
+datasets:
+- Despina/re_gentune
 ---
+# Llama-3.2-3B-Instruct — RE GenTune (2-shot)
+> Built with Llama. This is a fine-tuned derivative of Meta's Llama-3.2-3B-Instruct and is
+> governed by the [Llama 3.2 Community License](https://www.llama.com/llama3_2/license/).
+A 3B language model fine-tuned for **relation extraction (RE)**. This is the best-performing
+**general-domain** checkpoint from the paper *"Sub-Billion, Super-Frontier: Fine-Tuned Small
+Language Models Rival Zero-Shot Frontier LLMs on General and Literary Relation Extraction"*
+([arXiv:2606.22606](https://arxiv.org/abs/2606.22606)).
+It reaches a **0.844 general-domain average (positive-class micro-F1)** — the single highest
+general-domain score across all 30 tuned configurations in the paper — compared with **0.69 for
+GPT-5.4** and **0.66 for Claude Sonnet 4.6** under the same minimal zero-shot protocol. As the
+paper stresses, this does **not** imply that small models are intrinsically stronger than frontier
+LLMs; it shows that targeted task adaptation lets a compact 4-bit model deployable on a single
+consumer GPU outperform general-purpose frontier systems under this protocol. An in-domain
+RoBERTa baseline also exceeds both frontier models, indicating the advantage stems from task
+adaptation rather than generative decoding.
+It is a **QLoRA (LoRA) adapter** on top of
+[`meta-llama/Llama-3.2-3B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct),
+tuned on the **GenTune** general-domain mixture using the **2-shot** prompt style.
+## What it does
+Given a sentence and two marked entities, the model outputs **only the relation label** that
+holds between them (one label, no explanation).
+## Usage
+This repo is a PEFT LoRA adapter, so load the base model and attach the adapter:
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+BASE = "meta-llama/Llama-3.2-3B-Instruct"
+ADAPTER = "Despina/Llama-3.2-3B-Instruct-re_gentune-2-shot"
+tokenizer = AutoTokenizer.from_pretrained(ADAPTER)
+model = AutoModelForCausalLM.from_pretrained(BASE, torch_dtype=torch.bfloat16, device_map="auto")
+model = PeftModel.from_pretrained(model, ADAPTER)
+model.eval()
+system_prompt = (
+    "You are a relation extraction system. Be concise and direct. "
+    "Output ONLY the relation type that holds between the two mentioned entities. "
+    "Do not output any explanation, punctuation, or extra text — only the label."
+)
+user_prompt = (
+    "Sentence: Steve Jobs co-founded Apple in Cupertino.\n"
+    "Entity 1: Steve Jobs\n"
+    "Entity 2: Apple\n"
+    "Relation:"
+)
+messages = [
+    {"role": "system", "content": system_prompt},
+    {"role": "user", "content": user_prompt},
+]
+inputs = tokenizer.apply_chat_template(
+    messages, add_generation_prompt=True, return_tensors="pt"
+).to(model.device)
+out = model.generate(inputs, max_new_tokens=16, do_sample=False)
+print(tokenizer.decode(out[0, inputs.shape[-1]:], skip_special_tokens=True).strip())
+```
+For best results, match the format the model was trained on: a system prompt asking for the
+label only, and (optionally) two in-context examples before the query — this is the **2-shot**
+regime. A **schema-enumerated** variant, where the allowed label set for the target dataset is
+injected into the system prompt, gives the strongest results in the paper.
+## Training
+| | |
+|---|---|
+| Base model | `meta-llama/Llama-3.2-3B-Instruct` |
+| Method | QLoRA (4-bit NF4, bf16 compute, double quant) |
+| LoRA | r = 64, α = 128, dropout = 0.05; targets: q/k/v/o + gate/up/down proj |
+| Training data | `Despina/re_gentune` (GenTune general-domain mixture), 2-shot prompts |
+| Objective | Generate the relation label only |
+| Epochs | 2 |
+| Learning rate | 1e-4 |
+| Effective batch | 4 × 2 grad-accum = 8 |
+| Max sequence length | 1024 |
+**GenTune** aggregates seven general-domain RE datasets: TACRED, SemEval-2010 Task 8, CoNLL04,
+NYT11, GIDS, Re-DocRED, and REBEL.
 ## Evaluation
+Scored with **positive-class micro-F1** (the no-relation class is excluded from the average).
+On the general-domain benchmarks the model scores **0.844 general-domain average** — the top
+score in the paper — versus zero-shot GPT-5.4 (0.69) and Claude Sonnet 4.6 (0.66) under a minimal
+zero-shot protocol. As the paper stresses, this reflects targeted task adaptation rather than any
+intrinsic superiority of small models. See the paper for the full 30-configuration matrix,
+literary-domain results, and the RoBERTa discriminative baseline.
+## Limitations
+- Trained to emit a single relation label; it is not a general-purpose chat model.
+- Tuned on general-domain text; expect degradation on out-of-domain / literary inputs (see the
+  cross-domain analysis in the paper).
+- Inherits the biases and licensing constraints of its underlying datasets.
+## Links
+- **Paper:** [arXiv:2606.22606](https://arxiv.org/abs/2606.22606)
+- **Code / reproduction:** https://github.com/DespinaChristou/compact-relex
+- **Training dataset:** [`Despina/re_gentune`](https://huggingface.co/datasets/Despina/re_gentune)
+## License
+This model is a derivative of Meta Llama 3.2 and is licensed under the
+[Llama 3.2 Community License](https://www.llama.com/llama3_2/license/). Use is subject to Meta's
+Acceptable Use Policy. "Built with Llama."
+## Citation
+If you use this model, please cite:
+```bibtex
+@article{christou2026subbillion,
+  title        = {Sub-Billion, Super-Frontier: Small Language Models Rival
+                  Zero-Shot Frontier LLMs on General and Literary Relation Extraction},
+  author       = {Christou, Despina and Tsoumakas, Grigorios},
+  journal      = {arXiv preprint arXiv:2606.22606},
+  year         = {2026},
+  url          = {https://arxiv.org/abs/2606.22606}
+}
+```