--- language: - en - zh license: apache-2.0 library_name: transformers widget: - text: [|User|] Hi 👋 [|Assistant|] model-index: - name: MiniChat-2-3B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 44.88 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniChat-2-3B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 67.69 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniChat-2-3B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 47.59 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniChat-2-3B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 49.64 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniChat-2-3B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 66.46 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniChat-2-3B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 32.68 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniChat-2-3B name: Open LLM Leaderboard --- ## MiniChat-2-3B 📑 [arXiv](https://arxiv.org/abs/2311.07052) | 👻 [GitHub](https://github.com/GeneZC/MiniMA) | 🤗 [HuggingFace-MiniMA](https://huggingface.co/GeneZC/MiniMA-3B) | 🤗 [HuggingFace-MiniChat](https://huggingface.co/GeneZC/MiniChat-3B) | 🤖 [ModelScope-MiniMA](https://modelscope.cn/models/GeneZC/MiniMA-3B) | 🤖 [ModelScope-MiniChat](https://modelscope.cn/models/GeneZC/MiniChat-3B) | 🤗 [HuggingFace-MiniChat-1.5](https://huggingface.co/GeneZC/MiniChat-1.5-3B) | 🤗 [HuggingFace-MiniMA-2](https://huggingface.co/GeneZC/MiniMA-2-3B) | 🤗 [HuggingFace-MiniChat-2](https://huggingface.co/GeneZC/MiniChat-2-3B) 🆕 **Updates from MiniChat-3B**: - better base model MiniMA-2-3B; - better data mixture; - use of [NEFTune](https://arxiv.org/abs/2310.05914); - use of [DPO](https://arxiv.org/abs/2305.18290). ❗ Must comply with LICENSE of LLaMA2 since it is derived from LLaMA2. A language model continued from MiniMA-3B and finetuned on both instruction and preference data. Surpassing Vicuna-7B and approximating LLaMA-2-Chat-7B on MT-Bench. teaser_b **Standard Benchmarks** |Method|TFLOPs|MMLU (5-shot)|CEval (5-shot)|DROP (3-shot)|HumanEval (0-shot)|BBH (3-shot)|GSM8K (8-shot)| |--|--|--|--|--|--|--|--| |Mamba-2.8B|4.6E9|25.58|24.74|15.72|7.32|29.37|3.49| |ShearedLLaMA-2.7B|0.8E9|26.97|22.88|19.98|4.88|30.48|3.56| |BTLM-3B|11.3E9|27.20|26.00|17.84|10.98|30.87|4.55| |StableLM-3B|72.0E9|44.75|31.05|22.35|15.85|32.59|10.99| |Qwen-1.8B|23.8E9|44.05|54.75|12.97|14.02|30.80|22.97| |Phi-2-2.8B|159.9E9|56.74|34.03|30.74|46.95|44.13|55.42| |LLaMA-2-7B|84.0E9|46.00|34.40|31.57|12.80|32.02|14.10| || |MiniMA-3B|4.0E9|28.51|28.23|22.50|10.98|31.61|8.11| |MiniChat-3B|4.0E9|38.40|36.48|22.58|18.29|31.36|29.72| |MiniMA-2-3B|13.4E9|40.14|44.65|23.10|14.63|31.43|8.87| |MiniChat-2-3B|13.4E9|46.17|43.91|30.26|22.56|34.95|38.13| **Instruction-following Benchmarks** |Method|AlpacaEval|MT-Bench|MT-Bench-ZH| |--|--|--|--| |GPT-4|95.28|9.18|8.96| |Zephyr-7B-Beta|90.60|7.34|6.27#| |Vicuna-7B|76.84|6.17|5.22#| |LLaMA-2-Chat-7B|71.37|6.27|5.43#| |Qwen-Chat-7B|-|-|6.24| |Phi-2-DPO|81.37|-|1.59#$| |StableLM-Zephyr-3B|76.00|6.64|4.31#| |Rocket-3B|79.75|6.56|4.07#| |Qwen-Chat-1.8B|-|-|5.65| || |MiniChat-3B|48.82|-|-| |MiniChat-2-3B|77.30|6.23|6.04| # specialized mainly for English. $ finetuned without multi-turn instruction data. The following is an example code snippet to use MiniChat-2-3B: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer from conversation import get_default_conv_template # MiniChat tokenizer = AutoTokenizer.from_pretrained("GeneZC/MiniChat-2-3B", use_fast=False) # GPU. model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-2-3B", use_cache=True, device_map="auto", torch_dtype=torch.float16).eval() # CPU. # model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-2-3B", use_cache=True, device_map="cpu", torch_dtype=torch.float16).eval() conv = get_default_conv_template("minichat") question = "Implement a program to find the common elements in two arrays without using any extra data structures." conv.append_message(conv.roles[0], question) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() input_ids = tokenizer([prompt]).input_ids output_ids = model.generate( torch.as_tensor(input_ids).cuda(), do_sample=True, temperature=0.7, max_new_tokens=1024, ) output_ids = output_ids[0][len(input_ids[0]):] output = tokenizer.decode(output_ids, skip_special_tokens=True).strip() # output: "def common_elements(arr1, arr2):\n if len(arr1) == 0:\n return []\n if len(arr2) == 0:\n return arr1\n\n common_elements = []\n for element in arr1:\n if element in arr2:\n common_elements.append(element)\n\n return common_elements" # Multiturn conversation could be realized by continuously appending questions to `conv`. ``` ## Bibtex ```bibtex @article{zhang2023law, title={Towards the Law of Capacity Gap in Distilling Language Models}, author={Zhang, Chen and Song, Dawei and Ye, Zheyu and Gao, Yan}, year={2023}, url={https://arxiv.org/abs/2311.07052} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_GeneZC__MiniChat-2-3B) | Metric |Value| |---------------------------------|----:| |Avg. |51.49| |AI2 Reasoning Challenge (25-Shot)|44.88| |HellaSwag (10-Shot) |67.69| |MMLU (5-Shot) |47.59| |TruthfulQA (0-shot) |49.64| |Winogrande (5-shot) |66.46| |GSM8k (5-shot) |32.68|