初始化项目，由ModelHub XC社区提供模型

Model: bactrianus/HotpotQA-Reader-CoT-Llama-3-8B-Instruct
Source: Original Platform

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README.md

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+---
+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
+
+<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`](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}
+}
+```

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config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
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+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
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+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.40.0.dev0",
+  "use_cache": true,
+  "vocab_size": 128256
+}

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+  "transformers_version": "4.40.0.dev0"
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