commit 5068d4f9ed23956c97a36d5b7bdf4915e1291254 Author: ModelHub XC Date: Mon Apr 13 03:00:08 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: DAMO-NLP-SG/mt-llama-7b-delta Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..d4720c9 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +pytorch_model-00001-of-00002.bin filter=lfs diff=lfs merge=lfs -text +pytorch_model-00002-of-00002.bin filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..c0e3d08 --- /dev/null +++ b/README.md @@ -0,0 +1,112 @@ +--- +license: mit +--- + +# MT-LLaMA Model Card + +## Model details + +**Model type:** +MT-LLaMA is an open-source multi-task model trained by fine-tuning LLaMA on the massive tasks in [P3](https://huggingface.co/datasets/bigscience/P3) (i.e., T0 Train). Concretely, the used datasets during training and task taxonomy are listed below: +* Multi-choice QA: CommonsenseQA, Cosmos QA, DREAM, QuAIL, QuaRTz, QASC, QuaRel, SciQ, Social IQA, Wiki Hop, WiQA +* Extractive QA: Adversarial QA, DuoRC, Quoref, ROPES +* Close-Book QA: Hotpot QA, Wiki QA +* Sentiment Classification: Amazon, App Reviews, IMDB, Rotten Tomatoes, Yelp +* Topic Classification: AG News, DBPedia, TREC +* Structure-to-Text Generation: Common Gen, Wiki Bio +* Text Summarization: CNN Daily Mail, Gigaword, MultiNews, SamSum, XSum +* Paraphrase Identification: MRPC, PAWS, QQP + +**Organizations developing the model:** +The MT-LLaMA team with members from Alibaba Damo Academy and the Chinese University of Hong Kong. + +## Intended use + +You can try the codes from our [github repo](https://github.com/DAMO-NLP-SG/MT-LLaMA). + + +## Zero-shot Evaluation + +We primarily follow the protocols of [Bigscience T0](https://openreview.net/forum?id=9Vrb9D0WI4) to assess the generalization capability of our Multi-task LLaMA to: (1) _**Unseen Datasets**_ (i.e., datasets from seen tasks); (2) _**Unseen Tasks**_. + +#### Prompt Format + +Extractive QA: + +1. XQuAD, TyDiQA, MLQA, SQuAD + ```angular2html + Input: Answer the question according to the context. Question: ${question}. Context: ${context}. Answer: + Output: ${Answer} + ``` + +Sentiment: + +1. SST-2 + ```angular2html + Input: ${sentence} Based on this review, would the user recommend this product? No or Yes? + Output: Yes / No + ``` +Multiple-Choice QA: + +1. OpenbookQA + ```angular2html + Input: ${question} Which is the correct answer? - (A) ${choiceA} - (B) ${choiceB} - (C) ${choiceC} - (D) ${choiceD} + Output: ${choiceA} / ${choiceB} / ${choiceC} / ${choiceD} + ``` +Sentence Completion: + +1. COPA + ```angular2html + Input: ${premise} {% if question == "cause" %} This happened because... {% else %} As a consequence... Help me pick the more plausible option: - ${text1} - ${text2} + Output: ${text1} / ${text2} + ``` +Coreference Resolution: +1. Winogrande: + ```angular2html + Input: ${sentence} In the previous sentence, does _ refer to ${option1} or ${option2}? + Output: ${option1} / ${option2} + ``` +Word Sense Disambiguation: +1. WiC + ```angular2html + Input: Does the word "${word}" have the same meaning in these two sentences? Yes, No? ${sentence1} ${sentence2} + Output: ${sentence1} / ${sentence2} + ``` +Natural Language Inference: + +1. MNLI: + ```angular2html + Input: ${premise} Question: Does this imply that ${hypothesis}? Please response with 'Yes', 'No', or 'Maybe'. + Output: Yes / No / Maybe + ``` +2. RTE + ```angular2html + Input: Given ${premise} Is it guaranteed true that "${hypothesis}"? Yes or no? + Output: Yes / no + ``` +#### Results on _Unseen Datasets_ + +| Model | XQuAD-en (F1/EM) | TyDiQA-en (F1/EM) | MLQA-en (F1/EM) | SQuAD (F1/EM) | SST-2 (Acc.) | OpenbookQA (Acc.) | +|:------------|------------------|-------------------|-----------------|---------------|--------------|-------------------| +| LLaMA-7b | 9.5 / 2.0 | 14.3 / 2.6 | 13.4 / 3.3 | 29.4 / 11.5 | 50.5 | 32.4 | +| MT-LLaMA-7b | 42.3 / 31.1 | 38.9 / 26.9 | 45.4 / 31.5 | 85.9 / 77.6 | 92.6 | 38.2 | +#### Results on _Unseen Tasks_ +| Model | COPA (Acc.) | Winogrande (Acc.) | WiC (Acc.) | MNLI (Acc.) | RTE (Acc.) | +|:------------|-------------|--------------------|------------|-------------|------------| +| LLaMA-7b | 56.0 | 49.3 | 51.7 | 30.2 | 52.7 | +| MT-LLaMA-7b | 88.0 | 54.9 | 52.2 | 49.6 | 79.1 | + +## Acknowledgement + +* Our training codes are largely borrowed from [FastChat](https://github.com/lm-sys/FastChat) +* We are also grateful for the efforts of [LLaMA](https://github.com/facebookresearch/llama) (from FAIR) and [T0](https://github.com/bigscience-workshop/t-zero) (from BigScience), which serve as the foundation of our work + +If you find this resource useful, please cite the repo as follows: +``` +@software{damonlpsg2023mtllama, + author = {Xu, Weiwen and Li, Xin and Bing, Lidong}, + title = {Multi-task Instruction-tuned LLaMA}, + year = 2023, + url = {https://github.com/DAMO-NLP-SG/MT-LLaMA} +} +``` diff --git a/added_tokens.json b/added_tokens.json new file mode 100644 index 0000000..e41416d --- /dev/null +++ b/added_tokens.json @@ -0,0 +1,3 @@ +{ + "[PAD]": 32000 +} diff --git a/config.json b/config.json new file mode 100644 index 0000000..5ec12b2 --- /dev/null +++ b/config.json @@ -0,0 +1,22 @@ +{ + "_name_or_path": "../BTC/saved_models/llama_P3_16_2e-5/checkpoint-90000", + "architectures": [ + "LlamaForCausalLM" + ], + "bos_token_id": 1, + "eos_token_id": 2, + "hidden_act": "silu", + "hidden_size": 4096, + 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