Despina
/

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

@@ -1,207 +1,157 @@
 ---
 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
+- literary-nlp
+- qlora
 - lora
+- peft
+- llama
+- nlp
+datasets:
+- Despina/re_mixtune
 ---
+# Llama-3.2-3B-Instruct — RE MixTune (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)** across **both general-domain and
+literary text**. This is the best single "does-both" 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)).
+Trained on a domain-balanced mixture, it handles both domains at once, scoring **0.827
+general-domain average** and **0.825 literary average (positive-class micro-F1)** simultaneously —
+close to each domain specialist's in-domain peak. For reference, zero-shot frontier LLMs under the
+same minimal protocol reach 0.69 (GPT-5.4) and 0.66 (Claude Sonnet 4.6) on general-domain RE, and
+GPT-5.4 reaches 0.578 on the two-benchmark literary average. As the paper stresses, this reflects
+targeted task adaptation rather than any intrinsic superiority of small models over frontier LLMs.
+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 **MixTune** balanced general+literary 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). Unlike the domain specialists, this checkpoint is
+meant to serve both general and literary inputs from a single model.
+## 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_mixtune-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_mixtune` (domain-balanced general+literary 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 |
+**MixTune** is a domain-balanced (~50/50) mixture drawing equal numbers of general and literary
+examples: the seven general-domain datasets (TACRED, SemEval-2010 Task 8, CoNLL04, NYT11, GIDS,
+Re-DocRED, REBEL) and the two literary datasets (Biographical, PG-Fiction).
 ## Evaluation
+Scored with **positive-class micro-F1** (the no-relation class is excluded from the average).
+Evaluated on all nine benchmarks, the model scores **0.827 general-domain average** and **0.825
+literary average** simultaneously — the strongest single-model choice when one model must cover
+both domains. For reference, zero-shot GPT-5.4 / Claude Sonnet 4.6 reach 0.69 / 0.66 on general
+RE, and GPT-5.4 reaches 0.578 on literary RE, 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 and the RoBERTa discriminative
+baseline.
+## Limitations
+- Trained to emit a single relation label; it is not a general-purpose chat model.
+- A single-model generalist: a domain specialist (GenTune or LitTune) may edge it out slightly
+  on its own domain.
+- PG-Fiction labels are annotated by a GPT-4-class model, so the model partly learns that
+  annotator's label distribution on literary inputs.
+- 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_mixtune`](https://huggingface.co/datasets/Despina/re_mixtune)
+## 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}
+}
+```