GeneZC/MiniChat-1.5-3B-GGUF

Quantized GGUF model files for MiniChat-1.5-3B from GeneZC

Name	Quant method	Size
minichat-1.5-3b.fp16.gguf	fp16	6.04 GB
minichat-1.5-3b.q2_k.gguf	q2_k	1.30 GB
minichat-1.5-3b.q3_k_m.gguf	q3_k_m	1.51 GB
minichat-1.5-3b.q4_k_m.gguf	q4_k_m	1.85 GB
minichat-1.5-3b.q5_k_m.gguf	q5_k_m	2.15 GB
minichat-1.5-3b.q6_k.gguf	q6_k	2.48 GB
minichat-1.5-3b.q8_0.gguf	q8_0	3.21 GB

Original Model Card:

MiniChat-1.5-3B

📑 arXiv | 👻 GitHub | 🤗 HuggingFace-MiniMA | 🤗 HuggingFace-MiniChat | 🤗 HuggingFace-MiniChat-1.5 | 🤖 ModelScope-MiniMA | 🤖 ModelScope-MiniChat

🆕 Updates from MiniChat-3B:

• better data mixture;
• use of NEFTune;
• use of DPO.

❗ 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.

The following is an example code snippet to use MiniChat-3B:

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

@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}
}