初始化项目，由ModelHub XC社区提供模型
Model: GeneZC/MiniChat-1.5-3B Source: Original Platform
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ModelHub XC
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README.md
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README.md
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---
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language:
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- en
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- zh
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license: apache-2.0
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library_name: transformers
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widget:
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- text: <s> [|User|] Hi 👋 </s>[|Assistant|]
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model-index:
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- name: MiniChat-1.5-3B
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: AI2 Reasoning Challenge (25-Shot)
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: acc_norm
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value: 46.5
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniChat-1.5-3B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: HellaSwag (10-Shot)
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: acc_norm
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value: 68.28
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniChat-1.5-3B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU (5-Shot)
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type: cais/mmlu
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config: all
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 46.67
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniChat-1.5-3B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: TruthfulQA (0-shot)
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: mc2
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value: 50.71
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniChat-1.5-3B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: Winogrande (5-shot)
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type: winogrande
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config: winogrande_xl
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split: validation
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 65.04
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniChat-1.5-3B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GSM8k (5-shot)
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type: gsm8k
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 24.18
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniChat-1.5-3B
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name: Open LLM Leaderboard
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---
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## MiniChat-1.5-3B
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📑 [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)
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🆕 **Updates from MiniChat-3B**:
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- better data mixture;
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- use of [NEFTune](https://arxiv.org/abs/2310.05914);
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- use of [DPO](https://arxiv.org/abs/2305.18290).
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❗ Must comply with LICENSE of LLaMA2 since it is derived from LLaMA2.
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A language model distilled and finetuned from an adapted version of LLaMA2-7B following "Towards the Law of Capacity Gap in Distilling Language Models".
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Outperforming a wide range of 3B competitors in GPT4 evaluation and even competing with several 7B chat models.
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<img src="./teaser_b.jpg" alt="teaser_b" width="687" />
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The following is an example code snippet to use MiniChat-3B:
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from conversation import get_default_conv_template
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# MiniChat
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tokenizer = AutoTokenizer.from_pretrained("GeneZC/MiniChat-3B", use_fast=False)
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# GPU.
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model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-3B", use_cache=True, device_map="auto", torch_dtype=torch.float16).eval()
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# CPU.
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# model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-3B", use_cache=True, device_map="cpu", torch_dtype=torch.float16).eval()
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conv = get_default_conv_template("minichat")
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question = "Implement a program to find the common elements in two arrays without using any extra data structures."
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conv.append_message(conv.roles[0], question)
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conv.append_message(conv.roles[1], None)
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prompt = conv.get_prompt()
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input_ids = tokenizer([prompt]).input_ids
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output_ids = model.generate(
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torch.as_tensor(input_ids).cuda(),
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do_sample=True,
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temperature=0.7,
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max_new_tokens=1024,
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)
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output_ids = output_ids[0][len(input_ids[0]):]
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output = tokenizer.decode(output_ids, skip_special_tokens=True).strip()
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# 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"
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# Multiturn conversation could be realized by continuously appending questions to `conv`.
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```
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## Bibtex
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```bibtex
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@article{zhang2023law,
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title={Towards the Law of Capacity Gap in Distilling Language Models},
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author={Zhang, Chen and Song, Dawei and Ye, Zheyu and Gao, Yan},
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year={2023},
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url={https://arxiv.org/abs/2311.07052}
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}
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```
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_GeneZC__MiniChat-1.5-3B)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |50.23|
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|AI2 Reasoning Challenge (25-Shot)|46.50|
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|HellaSwag (10-Shot) |68.28|
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|MMLU (5-Shot) |46.67|
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|TruthfulQA (0-shot) |50.71|
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|Winogrande (5-shot) |65.04|
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|GSM8k (5-shot) |24.18|
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