初始化项目，由ModelHub XC社区提供模型
Model: afrideva/MiniChat-1.5-3B-GGUF Source: Original Platform
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README.md
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README.md
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---
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base_model: GeneZC/MiniChat-1.5-3B
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inference: false
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language:
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- en
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- zh
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library_name: transformers
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license: apache-2.0
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model_creator: GeneZC
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model_name: MiniChat-1.5-3B
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pipeline_tag: text-generation
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quantized_by: afrideva
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tags:
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- gguf
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- ggml
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- quantized
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- q2_k
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- q3_k_m
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- q4_k_m
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- q5_k_m
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- q6_k
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- q8_0
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widget:
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- text: "<s> [|User|] Hi \U0001F44B </s>[|Assistant|]"
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---
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# GeneZC/MiniChat-1.5-3B-GGUF
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Quantized GGUF model files for [MiniChat-1.5-3B](https://huggingface.co/GeneZC/MiniChat-1.5-3B) from [GeneZC](https://huggingface.co/GeneZC)
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| Name | Quant method | Size |
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| ---- | ---- | ---- |
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| [minichat-1.5-3b.fp16.gguf](https://huggingface.co/afrideva/MiniChat-1.5-3B-GGUF/resolve/main/minichat-1.5-3b.fp16.gguf) | fp16 | 6.04 GB |
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| [minichat-1.5-3b.q2_k.gguf](https://huggingface.co/afrideva/MiniChat-1.5-3B-GGUF/resolve/main/minichat-1.5-3b.q2_k.gguf) | q2_k | 1.30 GB |
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| [minichat-1.5-3b.q3_k_m.gguf](https://huggingface.co/afrideva/MiniChat-1.5-3B-GGUF/resolve/main/minichat-1.5-3b.q3_k_m.gguf) | q3_k_m | 1.51 GB |
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| [minichat-1.5-3b.q4_k_m.gguf](https://huggingface.co/afrideva/MiniChat-1.5-3B-GGUF/resolve/main/minichat-1.5-3b.q4_k_m.gguf) | q4_k_m | 1.85 GB |
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| [minichat-1.5-3b.q5_k_m.gguf](https://huggingface.co/afrideva/MiniChat-1.5-3B-GGUF/resolve/main/minichat-1.5-3b.q5_k_m.gguf) | q5_k_m | 2.15 GB |
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| [minichat-1.5-3b.q6_k.gguf](https://huggingface.co/afrideva/MiniChat-1.5-3B-GGUF/resolve/main/minichat-1.5-3b.q6_k.gguf) | q6_k | 2.48 GB |
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| [minichat-1.5-3b.q8_0.gguf](https://huggingface.co/afrideva/MiniChat-1.5-3B-GGUF/resolve/main/minichat-1.5-3b.q8_0.gguf) | q8_0 | 3.21 GB |
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## Original Model Card:
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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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