---
license: llama3
language:
  - en
library_name: transformers
pipeline_tag: text-generation
base_model: meta-llama/Meta-Llama-3-8B-Instruct
base_model_relation: finetune
datasets:
  - bactrianus/bactrainus-hotpotqa
tags:
  - bactrainus
  - bactrianus
  - llama-3
  - hotpotqa
  - multi-hop-qa
  - answer-generation
  - legacy
---

# Bactrainus HotpotQA Reader — Llama 3 8B Instruct

<p align="center">
  <img src="assets/models.png" alt="Bactrainus Llama 3 model collection artwork" width="720">
</p>

## Artifact identity

- **Status:** complete merged causal-language-model checkpoint
- **Base model:** `meta-llama/Meta-Llama-3-8B-Instruct`
- **Audited Hub revision:** `98c63afd55b5b4bd46890165e85e69c7d7d10a3d`
- **Public artifact date:** August 2024
- **Role:** direct answer reader over supplied evidence

This is a historical **Llama 3** artifact. The revised Bactrainus manuscript reports controlled architecture experiments with **Llama 3.1** checkpoints. This card does not relabel, rebase, or claim that these weights are the revised manuscript checkpoint.

## Model summary

The model is a HotpotQA-focused reader. It receives a multi-hop question together with already selected evidence and generates a concise answer. Evidence selection is outside this checkpoint's responsibility.

This model is appropriate for studying the reader stage independently or as the final component of a fixed-candidate selector--reader pipeline.

## Intended use

- Research on answer generation from compact multi-document evidence.
- Reader-stage ablations in the English HotpotQA distractor setting.
- Integration behind a paragraph/sentence selector that emits validated evidence.

### Out-of-scope use

- Open-domain retrieval or searching Wikipedia.
- Treating the model as a source of verified facts without supplied evidence.
- Safety-critical, legal, medical, or high-stakes decision support.
- Claiming direct reproduction of revised Llama 3.1 paper values.

## Input and output contract

Input should contain:

1. one question;
2. a deterministic serialization of selected supporting sentences, including their titles;
3. an instruction to return a concise answer.

Output is free-form text and must be normalized and parsed before evaluation. The public legacy configuration does not preserve a complete prompt-version manifest; an illustrative prompt should not be treated as a byte-exact reconstruction of the 2024 training serialization.

## Loading

Pin the immutable revision:

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_ID = "bactrianus/HotpotQA-Reader-Llama-3-8B-Instruct"
REVISION = "98c63afd55b5b4bd46890165e85e69c7d7d10a3d"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, revision=REVISION)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    revision=REVISION,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model.eval()
```

Review the Meta Llama 3 license and choose hardware, precision, and generation limits appropriate for your environment before running inference.

## Training data and lineage

The checkpoint is derived from Meta Llama 3 8B Instruct and adapted for HotpotQA-derived reader supervision. HotpotQA supplies English Wikipedia questions, candidate paragraphs, answers, and supporting-fact annotations.

The matching canonical training view is [`reader-sft`](https://huggingface.co/datasets/bactrianus/bactrainus-hotpotqa/tree/v1.0.0/data/reader-sft), pinned to dataset tag `v1.0.0`. It contains all 90,447 training source IDs and remains joinable to every other view through `source_id`; byte-for-byte identity with every historical 2024 training file is not asserted.

```python
from datasets import load_dataset

train = load_dataset(
    "bactrianus/bactrainus-hotpotqa",
    "reader-sft",
    split="train",
    revision="v1.0.0",
)
```

Do not project the revised Llama 3.1 optimizer and LoRA configuration onto this historical merged checkpoint unless an independently verified run manifest establishes that identity.

## Evaluation boundary

No predictions, evaluation results, or leaderboard claims are bundled with this model card. For the methodology and the distinction between historical artifacts and revised experiments, see the [Bactrainus paper](https://arxiv.org/abs/2501.06286) and the [clean code repository](https://github.com/Iman998/bactrainus).

## Limitations

- The model was specialized for English HotpotQA-style inputs and was not validated as a general reader.
- Accuracy depends strongly on evidence quality; missing facts cannot be recovered reliably downstream.
- Extra paragraphs can introduce substantial context noise.
- Generated answers may be unsupported, malformed, or more verbose than the expected short answer.
- Wikipedia-derived data inherits temporal, cultural, and coverage biases.
- The historical release lacks a complete immutable environment and prompt manifest.

## License and attribution

The weights remain subject to the [Meta Llama 3 Community License](https://github.com/meta-llama/llama3/blob/main/LICENSE) and Acceptable Use Policy. Redistribution must include the upstream agreement and required notices.

> Meta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright Meta Platforms, Inc. All Rights Reserved.

**Built with Meta Llama 3.**

HotpotQA-derived data is licensed under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/). The Bactrainus code is separately licensed under Apache-2.0.

## Citation

```bibtex
@article{barati2025bactrainus,
  title   = {Bactrainus: Optimizing Large Language Models for Multi-hop Complex Question Answering Tasks},
  author  = {Barati, Iman and Ghafouri, Arash and Minaei-Bidgoli, Behrouz},
  journal = {arXiv preprint arXiv:2501.06286},
  year    = {2025},
  doi     = {10.48550/arXiv.2501.06286},
  url     = {https://arxiv.org/abs/2501.06286}
}
```