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bactrianus/HotpotQA-Reader-CoT-Llama-3-8B-Instruct

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Model Card

Bactrainus HotpotQA Rationale 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: 852277e5b9534ff51a66adbad1ad43b7a3ef4457
  • —Public artifact date: August 2024
  • —Role: rationale-plus-answer generation from supplied evidence

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.

Model summary

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.

Intended use

  • —Studying natural-language rationale supervision for HotpotQA readers.
  • —Qualitative inspection of an evidence-conditioned answer path.
  • —Reader-stage comparisons where evidence is supplied independently.

Out-of-scope use

  • —Treating generated rationales as faithful explanations or verified proofs.
  • —Using rationale text as a substitute for HotpotQA supporting-fact labels.
  • —Open-domain retrieval, safety-critical decisions, or factual verification.
  • —Associating revised Llama 3.1 rationale results with this legacy checkpoint.

Input and output contract

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.

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.

Loading

python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_ID = "bactrianus/HotpotQA-Reader-CoT-Llama-3-8B-Instruct"
REVISION = "852277e5b9534ff51a66adbad1ad43b7a3ef4457"

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()

Training data and lineage

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:

  • —`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.
  • —`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.
  • —`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.
python
from datasets import load_dataset

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

teacher_8b_train = load_dataset(
    "bactrianus/bactrainus-hotpotqa-teacher-traces",
    "teacher-cot-llama31-8b-sft",
    split="train",
    revision="v1.0.0",
)

teacher_70b_train = load_dataset(
    "bactrianus/bactrainus-hotpotqa-teacher-traces",
    "teacher-cot-llama31-70b-sft",
    split="train",
    revision="v1.0.0",
)

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.

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.

Evaluation boundary

No predictions or evaluation results are bundled with this card. The Bactrainus paper reports rationale-supervision experiments with explicit recipe caveats. A correct final answer does not establish rationale faithfulness.

Limitations

  • —Generated rationales can be post-hoc, incomplete, contradictory, or unsupported.
  • —Longer outputs increase parsing and truncation risk.
  • —Evidence omissions propagate to both rationale and answer.
  • —The model is specialized for English HotpotQA-style inputs.
  • —Wikipedia-derived data carries temporal and representational biases.
  • —Historical prompt, generator, and environment details are incomplete.

License and attribution

The weights remain subject to the Meta Llama 3 Community License and Acceptable Use Policy.

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. Bactrainus code is Apache-2.0 licensed.

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}
}