---
license: llama3.2
license_link: https://www.llama.com/llama3_2/license/
language:
- es
library_name: peft
base_model:
- meta-llama/Llama-3.2-3B-Instruct
tags:
- clinical-ner
- spanish
- lora
- peft
- reproducibility
---

# ContextAugmNER distemist-llama3b-r1-seed2

This repository contains one LoRA adapter from the ContextAugmNER paper release,
a reproducibility study of inference-protocol sensitivity in compact
decoder-only Spanish clinical named entity recognition.

The adapter is released as a PEFT/LoRA artifact. It does not contain merged
base-model weights. Users must obtain the upstream base model and comply with
the base-model licence and access terms.

## Adapter Summary

- Dataset: `distemist`
- Base-model alias: `llama3b`
- Base model: `meta-llama/Llama-3.2-3B-Instruct`
- Training regime: `r1` (R1-normal)
- Seed: `2`
- Paper run ID: `train_distemist_opt1_r1_llama3b_seed2`
- Local release ID: `distemist-llama3b-r1-seed2`

## Repository Contents

- `adapter_model.safetensors`: LoRA adapter weights.
- `adapter_config.json`: PEFT adapter configuration, including base model.
- tokenizer files: copied from the training/inference artifact for exact
  reproducibility of the paper runs.
- `adapter_manifest.csv`: one-row manifest mapping this HF repository to the
  paper run ID and release metadata.

`training_args.bin` is excluded from the default upload because it is not needed
for inference and can contain environment-specific training state.

## Loading

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = "meta-llama/Llama-3.2-3B-Instruct"
adapter_id = "edugredu/contextaugmner-distemist-llama3b-r1-seed2"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)
model = AutoModelForCausalLM.from_pretrained(
    base_model,
    torch_dtype="auto",
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter_id)
model.eval()
```

The paper's scored runs used the project inference code, strict parser,
deterministic span alignment, official shared-task scorers, greedy decoding,
chunking at 1800 characters with 200-character overlap, and a generation budget
of 960 new tokens. For paper reproduction, use the linked code release and the
manifest rather than ad hoc prompting.

## How This Adapter Is Used in the Paper

AuxStop, batch-one execution, matched stop/truncation, online-stop-only,
offline-truncate, and left-padding conditions are inference-time protocols.
They reuse trained adapters and are not separate trained checkpoints.

For the central corrected inference recommendation, trained R0 adapters are
reused with left-padding at batch size 64 or effective batch-one execution,
depending on the reported contrast. R1 adapters are retained for the historical
primary grid and transparency about the original rationale-training comparison.

## Data and Ethics

The adapter was trained on public, de-identified Spanish clinical NER benchmark
data distributed by the original shared-task organizers. Dataset access and use
must follow the MedProcNER, SympTEMIST, and DisTEMIST task terms.

## Intended Use

This artifact is intended for research reproducibility, audit of inference
protocols, and controlled comparison with the ContextAugmNER paper results. It
is not a clinical decision-support system and should not be used for patient
care without independent validation, governance review, and task-specific error
analysis.

## Limitations

- The adapter is specialised to one dataset/task configuration and one random
  seed.
- Reported paper metrics depend on the full inference protocol, parser,
  aligner, scorer, and padding/batching settings.
- The release does not establish universal clinical NER performance outside the
  evaluated Spanish benchmark setting.
- The adapter inherits limitations and licence obligations from the upstream
  base model.

## Llama Attribution

Built with Llama.

Llama 3.2 is licensed under the Llama 3.2 Community License,
Copyright (c) Meta Platforms, Inc. All Rights Reserved.


## Citation

If you use this adapter, cite the ContextAugmNER paper and the corresponding
base model.