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Model: bactrianus/HotpotQA-Reader-CoT-Llama-3-8B-Instruct Source: Original Platform
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README.md
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README.md
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---
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license: llama3
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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base_model: meta-llama/Meta-Llama-3-8B-Instruct
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base_model_relation: finetune
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datasets:
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- bactrianus/bactrainus-hotpotqa
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- bactrianus/bactrainus-hotpotqa-teacher-traces
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tags:
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- bactrainus
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- bactrianus
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- llama-3
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- hotpotqa
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- multi-hop-qa
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- rationale-supervision
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- answer-generation
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- legacy
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---
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# Bactrainus HotpotQA Rationale Reader — Llama 3 8B Instruct
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<p align="center">
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<img src="assets/models.png" alt="Bactrainus Llama 3 model collection artwork" width="720">
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</p>
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## Artifact identity
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- **Status:** complete merged causal-language-model checkpoint
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- **Base model:** `meta-llama/Meta-Llama-3-8B-Instruct`
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- **Audited Hub revision:** `852277e5b9534ff51a66adbad1ad43b7a3ef4457`
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- **Public artifact date:** August 2024
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- **Role:** rationale-plus-answer generation from supplied evidence
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This is a historical **Llama 3** artifact. It must not be represented as either revised Llama 3.1 rationale-reader variant described in the updated manuscript.
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## Model summary
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This reader is adapted to generate an intermediate natural-language rationale followed by an answer. The rationale is process supervision generated for task adaptation; it is not a hidden trace recovered from the base model and is not a gold supporting-fact annotation.
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## Intended use
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- Studying natural-language rationale supervision for HotpotQA readers.
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- Qualitative inspection of an evidence-conditioned answer path.
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- Reader-stage comparisons where evidence is supplied independently.
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### Out-of-scope use
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- Treating generated rationales as faithful explanations or verified proofs.
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- Using rationale text as a substitute for HotpotQA supporting-fact labels.
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- Open-domain retrieval, safety-critical decisions, or factual verification.
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- Associating revised Llama 3.1 rationale results with this legacy checkpoint.
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## Input and output contract
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Input should contain a question and selected, title-preserving evidence. Output is expected to contain rationale text and a final answer. Downstream code must parse the final answer explicitly and must keep rationale evaluation separate from answer/evidence metrics.
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The public legacy configuration does not preserve a complete prompt-version manifest or an independently verified rationale delimiter. Do not assume that a newly invented delimiter exactly matches historical training.
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## Loading
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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MODEL_ID = "bactrianus/HotpotQA-Reader-CoT-Llama-3-8B-Instruct"
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REVISION = "852277e5b9534ff51a66adbad1ad43b7a3ef4457"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, revision=REVISION)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_ID,
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revision=REVISION,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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model.eval()
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```
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## Training data and lineage
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The checkpoint derives from Meta Llama 3 8B Instruct and HotpotQA-based reader/rationale supervision. Two separately versioned dataset resources are relevant to this task:
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- [`cot-reader-sft`](https://huggingface.co/datasets/bactrianus/bactrainus-hotpotqa/tree/v1.0.0/data/cot-reader-sft) in the canonical dataset at `v1.0.0` is a complete deterministic view with 90,447 unique training source IDs. Its assistant targets serialize indexed gold evidence followed by the reference answer; they are not model-generated rationales.
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- [`teacher-cot-llama31-8b-sft`](https://huggingface.co/datasets/bactrianus/bactrainus-hotpotqa-teacher-traces/tree/v1.0.0/data/teacher-cot-llama31-8b-sft) in the teacher-trace dataset at `v1.0.0` contains one resolved Llama 3.1 8B-labelled rationale per source ID for 71,238 training examples.
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- [`teacher-cot-llama31-70b-sft`](https://huggingface.co/datasets/bactrianus/bactrainus-hotpotqa-teacher-traces/tree/v1.0.0/data/teacher-cot-llama31-70b-sft) in the teacher-trace dataset at `v1.0.0` contains 28,176 recovered Llama 3.1 70B-labelled records: all 15,661 hard examples and 12,515 medium examples. Each row includes the archived annotation and an SFT conversation joined to the canonical dataset through `source_id`.
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```python
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from datasets import load_dataset
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deterministic_train = load_dataset(
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"bactrianus/bactrainus-hotpotqa",
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"cot-reader-sft",
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split="train",
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revision="v1.0.0",
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)
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teacher_8b_train = load_dataset(
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"bactrianus/bactrainus-hotpotqa-teacher-traces",
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"teacher-cot-llama31-8b-sft",
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split="train",
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revision="v1.0.0",
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)
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teacher_70b_train = load_dataset(
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"bactrianus/bactrainus-hotpotqa-teacher-traces",
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"teacher-cot-llama31-70b-sft",
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split="train",
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revision="v1.0.0",
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)
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```
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These resources document the current dataset release; neither is asserted to be byte-identical to the historical training serialization for this legacy Llama 3 checkpoint. The teacher configuration's Llama 3.1 label describes its archived generator record and must not be used to relabel these Llama 3 weights or infer an unrecorded training dependency.
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Likewise, the revised `reader_8b_rationale_8b.yaml` and `reader_8b_rationale_70b.yaml` files describe Llama 3.1 experiments, not this Llama 3 weight artifact.
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## Evaluation boundary
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No predictions or evaluation results are bundled with this card. The [Bactrainus paper](https://arxiv.org/abs/2501.06286) reports rationale-supervision experiments with explicit recipe caveats. A correct final answer does not establish rationale faithfulness.
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## Limitations
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- Generated rationales can be post-hoc, incomplete, contradictory, or unsupported.
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- Longer outputs increase parsing and truncation risk.
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- Evidence omissions propagate to both rationale and answer.
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- The model is specialized for English HotpotQA-style inputs.
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- Wikipedia-derived data carries temporal and representational biases.
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- Historical prompt, generator, and environment details are incomplete.
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## License and attribution
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The weights remain subject to the [Meta Llama 3 Community License](https://github.com/meta-llama/llama3/blob/main/LICENSE) and Acceptable Use Policy.
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> Meta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright Meta Platforms, Inc. All Rights Reserved.
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**Built with Meta Llama 3.**
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HotpotQA-derived data is licensed under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/). Bactrainus code is Apache-2.0 licensed.
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## Citation
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```bibtex
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@article{barati2025bactrainus,
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title = {Bactrainus: Optimizing Large Language Models for Multi-hop Complex Question Answering Tasks},
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author = {Barati, Iman and Ghafouri, Arash and Minaei-Bidgoli, Behrouz},
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journal = {arXiv preprint arXiv:2501.06286},
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year = {2025},
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doi = {10.48550/arXiv.2501.06286},
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url = {https://arxiv.org/abs/2501.06286}
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}
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```
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