Quantization made by Richard Erkhov.

MiniChat-2-3B - GGUF

Model creator: https://huggingface.co/GeneZC/
Original model: https://huggingface.co/GeneZC/MiniChat-2-3B/

Name	Quant method	Size
MiniChat-2-3B.Q2_K.gguf	Q2_K	1.09GB
MiniChat-2-3B.IQ3_XS.gguf	IQ3_XS	1.21GB
MiniChat-2-3B.IQ3_S.gguf	IQ3_S	1.27GB
MiniChat-2-3B.Q3_K_S.gguf	Q3_K_S	1.27GB
MiniChat-2-3B.IQ3_M.gguf	IQ3_M	1.33GB
MiniChat-2-3B.Q3_K.gguf	Q3_K	1.4GB
MiniChat-2-3B.Q3_K_M.gguf	Q3_K_M	1.4GB
MiniChat-2-3B.Q3_K_L.gguf	Q3_K_L	1.52GB
MiniChat-2-3B.IQ4_XS.gguf	IQ4_XS	1.55GB
MiniChat-2-3B.Q4_0.gguf	Q4_0	1.62GB
MiniChat-2-3B.IQ4_NL.gguf	IQ4_NL	1.63GB
MiniChat-2-3B.Q4_K_S.gguf	Q4_K_S	1.63GB
MiniChat-2-3B.Q4_K.gguf	Q4_K	1.72GB
MiniChat-2-3B.Q4_K_M.gguf	Q4_K_M	1.72GB
MiniChat-2-3B.Q4_1.gguf	Q4_1	1.79GB
MiniChat-2-3B.Q5_0.gguf	Q5_0	1.95GB
MiniChat-2-3B.Q5_K_S.gguf	Q5_K_S	1.95GB
MiniChat-2-3B.Q5_K.gguf	Q5_K	2.01GB
MiniChat-2-3B.Q5_K_M.gguf	Q5_K_M	2.01GB
MiniChat-2-3B.Q5_1.gguf	Q5_1	2.12GB
MiniChat-2-3B.Q6_K.gguf	Q6_K	2.31GB
MiniChat-2-3B.Q8_0.gguf	Q8_0	2.99GB

Original model description:

language: - en - zh license: apache-2.0 library_name: transformers widget: - text: [|User|] Hi 👋 [|Assistant|] model-index: - name: MiniChat-2-3B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 44.88 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniChat-2-3B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 67.69 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniChat-2-3B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 47.59 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniChat-2-3B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 49.64 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniChat-2-3B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 66.46 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniChat-2-3B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 32.68 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniChat-2-3B name: Open LLM Leaderboard

MiniChat-2-3B

🆕 Updates from MiniChat-3B:

better base model MiniMA-2-3B;
better data mixture;
use of NEFTune;
use of DPO.

❗ Must comply with LICENSE of LLaMA2 since it is derived from LLaMA2.

A language model continued from MiniMA-3B and finetuned on both instruction and preference data.

Surpassing Vicuna-7B and approximating LLaMA-2-Chat-7B on MT-Bench.

Standard Benchmarks

Method	TFLOPs	MMLU (5-shot)	CEval (5-shot)	DROP (3-shot)	HumanEval (0-shot)	BBH (3-shot)	GSM8K (8-shot)
Mamba-2.8B	4.6E9	25.58	24.74	15.72	7.32	29.37	3.49
ShearedLLaMA-2.7B	0.8E9	26.97	22.88	19.98	4.88	30.48	3.56
BTLM-3B	11.3E9	27.20	26.00	17.84	10.98	30.87	4.55
StableLM-3B	72.0E9	44.75	31.05	22.35	15.85	32.59	10.99
Qwen-1.8B	23.8E9	44.05	54.75	12.97	14.02	30.80	22.97
Phi-2-2.8B	159.9E9	56.74	34.03	30.74	46.95	44.13	55.42
LLaMA-2-7B	84.0E9	46.00	34.40	31.57	12.80	32.02	14.10

MiniMA-3B	4.0E9	28.51	28.23	22.50	10.98	31.61	8.11
MiniChat-3B	4.0E9	38.40	36.48	22.58	18.29	31.36	29.72
MiniMA-2-3B	13.4E9	40.14	44.65	23.10	14.63	31.43	8.87
MiniChat-2-3B	13.4E9	46.17	43.91	30.26	22.56	34.95	38.13

Instruction-following Benchmarks

Method	AlpacaEval	MT-Bench	MT-Bench-ZH
GPT-4	95.28	9.18	8.96
Zephyr-7B-Beta	90.60	7.34	6.27^#
Vicuna-7B	76.84	6.17	5.22^#
LLaMA-2-Chat-7B	71.37	6.27	5.43^#
Qwen-Chat-7B	-	-	6.24
Phi-2-DPO	81.37	-	1.59^#^$
StableLM-Zephyr-3B	76.00	6.64	4.31^#
Rocket-3B	79.75	6.56	4.07^#
Qwen-Chat-1.8B	-	-	5.65

MiniChat-3B	48.82	-	-
MiniChat-2-3B	77.30	6.23	6.04

^# specialized mainly for English.

^$ finetuned without multi-turn instruction data.

The following is an example code snippet to use MiniChat-2-3B:

import torch

from transformers import AutoModelForCausalLM, AutoTokenizer

from conversation import get_default_conv_template

# MiniChat
tokenizer = AutoTokenizer.from_pretrained("GeneZC/MiniChat-2-3B", use_fast=False)
# GPU.
model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-2-3B", use_cache=True, device_map="auto", torch_dtype=torch.float16).eval()
# CPU.
# model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-2-3B", 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}
}

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	51.49
AI2 Reasoning Challenge (25-Shot)	44.88
HellaSwag (10-Shot)	67.69
MMLU (5-Shot)	47.59
TruthfulQA (0-shot)	49.64
Winogrande (5-shot)	66.46
GSM8k (5-shot)	32.68