初始化项目，由ModelHub XC社区提供模型
Model: bactrianus/HotpotQA-Reader-Llama-3-8B-Instruct Source: Original Platform
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README.md
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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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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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- answer-generation
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- legacy
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---
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# Bactrainus HotpotQA 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:** `98c63afd55b5b4bd46890165e85e69c7d7d10a3d`
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- **Public artifact date:** August 2024
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- **Role:** direct answer reader over supplied evidence
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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.
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## Model summary
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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.
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This model is appropriate for studying the reader stage independently or as the final component of a fixed-candidate selector--reader pipeline.
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## Intended use
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- Research on answer generation from compact multi-document evidence.
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- Reader-stage ablations in the English HotpotQA distractor setting.
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- Integration behind a paragraph/sentence selector that emits validated evidence.
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### Out-of-scope use
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- Open-domain retrieval or searching Wikipedia.
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- Treating the model as a source of verified facts without supplied evidence.
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- Safety-critical, legal, medical, or high-stakes decision support.
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- Claiming direct reproduction of revised Llama 3.1 paper values.
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## Input and output contract
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Input should contain:
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1. one question;
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2. a deterministic serialization of selected supporting sentences, including their titles;
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3. an instruction to return a concise answer.
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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.
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## Loading
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Pin the immutable revision:
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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-Llama-3-8B-Instruct"
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REVISION = "98c63afd55b5b4bd46890165e85e69c7d7d10a3d"
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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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Review the Meta Llama 3 license and choose hardware, precision, and generation limits appropriate for your environment before running inference.
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## Training data and lineage
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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.
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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.
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```python
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from datasets import load_dataset
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train = load_dataset(
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"bactrianus/bactrainus-hotpotqa",
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"reader-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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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.
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## Evaluation boundary
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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).
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## Limitations
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- The model was specialized for English HotpotQA-style inputs and was not validated as a general reader.
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- Accuracy depends strongly on evidence quality; missing facts cannot be recovered reliably downstream.
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- Extra paragraphs can introduce substantial context noise.
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- Generated answers may be unsupported, malformed, or more verbose than the expected short answer.
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- Wikipedia-derived data inherits temporal, cultural, and coverage biases.
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- The historical release lacks a complete immutable environment and prompt manifest.
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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. Redistribution must include the upstream agreement and required notices.
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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/). The Bactrainus code is separately licensed under Apache-2.0.
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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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