Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) MiniChat-1.5-3B - bnb 4bits - Model creator: https://huggingface.co/GeneZC/ - Original model: https://huggingface.co/GeneZC/MiniChat-1.5-3B/ Original model description: --- language: - en - zh license: apache-2.0 library_name: transformers widget: - text: [|User|] Hi 👋 [|Assistant|] model-index: - name: MiniChat-1.5-3B 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: 46.5 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniChat-1.5-3B 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: 68.28 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniChat-1.5-3B 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: 46.67 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniChat-1.5-3B 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: 50.71 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniChat-1.5-3B 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: 65.04 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniChat-1.5-3B 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: 24.18 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniChat-1.5-3B name: Open LLM Leaderboard --- ## MiniChat-1.5-3B 📑 [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) | 🤗 [HuggingFace-MiniChat-1.5](https://huggingface.co/GeneZC/MiniChat-1.5-3B) | 🤖 [ModelScope-MiniMA](https://modelscope.cn/models/GeneZC/MiniMA-3B) | 🤖 [ModelScope-MiniChat](https://modelscope.cn/models/GeneZC/MiniChat-3B) 🆕 **Updates from MiniChat-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 distilled and finetuned from an adapted version of LLaMA2-7B following "Towards the Law of Capacity Gap in Distilling Language Models". Outperforming a wide range of 3B competitors in GPT4 evaluation and even competing with several 7B chat models. teaser_b The following is an example code snippet to use MiniChat-3B: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer from conversation import get_default_conv_template # MiniChat tokenizer = AutoTokenizer.from_pretrained("GeneZC/MiniChat-3B", use_fast=False) # GPU. model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-3B", use_cache=True, device_map="auto", torch_dtype=torch.float16).eval() # CPU. # model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-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-1.5-3B) | Metric |Value| |---------------------------------|----:| |Avg. |50.23| |AI2 Reasoning Challenge (25-Shot)|46.50| |HellaSwag (10-Shot) |68.28| |MMLU (5-Shot) |46.67| |TruthfulQA (0-shot) |50.71| |Winogrande (5-shot) |65.04| |GSM8k (5-shot) |24.18|