初始化项目，由ModelHub XC社区提供模型

Model: GeneZC/MiniChat-3B
Source: Original Platform

README.md

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+---
+frameworks:
+- Pytorch
+license: Apache License 2.0
+tasks:
+- fill-mask
+---
+
+## MiniChat-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)
+
+❗ 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.
+
+<img src="./teaser_b.jpg" alt="teaser_b" width="687" />
+
+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}
+}
+```