--- 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 - bactrianus/bactrainus-hotpotqa-teacher-traces tags: - bactrainus - bactrianus - llama-3 - hotpotqa - multi-hop-qa - rationale-supervision - answer-generation - legacy --- # Bactrainus HotpotQA Rationale Reader — Llama 3 8B Instruct

Bactrainus Llama 3 model collection artwork

## 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`](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. - [`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. - [`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`. ```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](https://arxiv.org/abs/2501.06286) 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](https://github.com/meta-llama/llama3/blob/main/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](https://creativecommons.org/licenses/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} } ```