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README.md

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+---
+base_model: GeneZC/MiniChat-3B
+inference: false
+language:
+- en
+- zh
+library_name: transformers
+license: apache-2.0
+model_creator: GeneZC
+model_name: MiniChat-3B
+pipeline_tag: text-generation
+quantized_by: afrideva
+tags:
+- gguf
+- ggml
+- quantized
+- q2_k
+- q3_k_m
+- q4_k_m
+- q5_k_m
+- q6_k
+- q8_0
+---
+# GeneZC/MiniChat-3B-GGUF
+
+Quantized GGUF model files for [MiniChat-3B](https://huggingface.co/GeneZC/MiniChat-3B) from [GeneZC](https://huggingface.co/GeneZC)
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [minichat-3b.q2_k.gguf](https://huggingface.co/afrideva/MiniChat-3B-GGUF/resolve/main/minichat-3b.q2_k.gguf) | q2_k | 1.30 GB  |
+| [minichat-3b.q3_k_m.gguf](https://huggingface.co/afrideva/MiniChat-3B-GGUF/resolve/main/minichat-3b.q3_k_m.gguf) | q3_k_m | 1.51 GB  |
+| [minichat-3b.q4_k_m.gguf](https://huggingface.co/afrideva/MiniChat-3B-GGUF/resolve/main/minichat-3b.q4_k_m.gguf) | q4_k_m | 1.85 GB  |
+| [minichat-3b.q5_k_m.gguf](https://huggingface.co/afrideva/MiniChat-3B-GGUF/resolve/main/minichat-3b.q5_k_m.gguf) | q5_k_m | 2.15 GB  |
+| [minichat-3b.q6_k.gguf](https://huggingface.co/afrideva/MiniChat-3B-GGUF/resolve/main/minichat-3b.q6_k.gguf) | q6_k | 2.48 GB  |
+| [minichat-3b.q8_0.gguf](https://huggingface.co/afrideva/MiniChat-3B-GGUF/resolve/main/minichat-3b.q8_0.gguf) | q8_0 | 3.21 GB  |
+
+
+
+## Original Model Card:
+## MiniChat-3B
+
+📑 [arXiv]() | 🤗 [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={}
+}
+```