J.O.S.I.E.3 Beta12 7B Slerp
J.O.S.I.E.3-Beta12-7B-slerp 是一个通过合并 Weyaxi/Einstein-v6-7B 和 argilla/CapybaraHermes-2.5-Mistral-7B 模型而成的7B参数大语言模型,支持多语言交互,采用ChatML提示格式。
下载量 17
发布时间 : 4/23/2024
模型简介
该模型是一个私人超级智能AI助手,专注于提供高质量的对话和问答服务,支持多种语言和复杂任务处理。
模型特点
多语言支持
支持包括中文在内的6种语言交互
合并模型优势
结合Einstein-v6和CapybaraHermes两个模型的优势,通过slerp方法合并
ChatML格式
采用标准化的ChatML提示格式,便于集成到对话系统中
量化支持
提供GGUF量化版本,便于在不同硬件上部署
模型能力
多语言文本生成
智能对话
知识问答
任务完成
使用案例
个人助手
私人AI助手
作为个人日常助手,回答各种问题并提供建议
在HellaSwag测试集上达到83.98%的归一化准确率
教育
学科知识问答
回答高中和大学水平的各学科问题
在高中地理测试中达到79.8%准确率
🚀 J.O.S.I.E.3-Beta12-7B-slerp
J.O.S.I.E.3-Beta12-7B-slerp 是一个融合模型,它使用 LazyMergekit 对以下模型进行了融合:
该模型在自定义的 J.O.S.I.E.v3.11 数据集上进行了进一步微调,采用 ChatML 提示格式。
<|im_start|>system
You are JOSIE, my private and superinteligent AI Assistant.<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
{{ .Response }}<|im_end|>
🚀 快速开始
在 ollama 中运行
ollama run goekdenizguelmez/j.o.s.i.e.v3-beta12.1
目前仅支持 q4-k-m 量化版本!
✨ 主要特性
- 融合了多个优秀模型的能力。
- 在自定义数据集上进行微调,以适应特定的任务和场景。
- 支持多种语言,包括英语、德语、西班牙语、法语、日语和中文。
📦 安装指南
安装依赖
!pip install -qU transformers accelerate
💻 使用示例
基础用法
from transformers import AutoTokenizer
import transformers
import torch
model = "Isaak-Carter/J.O.S.I.E.3-Beta12-7B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
📚 详细文档
🧩 配置信息
slices:
- sources:
- model: Weyaxi/Einstein-v6-7B
layer_range: [0, 32]
- model: argilla/CapybaraHermes-2.5-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: argilla/CapybaraHermes-2.5-Mistral-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
评估结果
{
"all": {
"acc": 0.635008846776534,
"acc_stderr": 0.03244450973873997,
"acc_norm": 0.6365238167399629,
"acc_norm_stderr": 0.033101612504829854,
"mc1": 0.397796817625459,
"mc1_stderr": 0.017133934248559635,
"mc2": 0.5816259277988214,
"mc2_stderr": 0.01521267822060948
},
"harness|arc:challenge|25": {
"acc": 0.6220136518771331,
"acc_stderr": 0.0141696645203031,
"acc_norm": 0.6459044368600683,
"acc_norm_stderr": 0.013975454122756557
},
"harness|hellaswag|10": {
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"acc_stderr": 0.004755960559929163,
"acc_norm": 0.8397729535949015,
"acc_norm_stderr": 0.003660668242740655
},
"harness|hendrycksTest-abstract_algebra|5": {
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"acc_norm": 0.4,
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},
"harness|hendrycksTest-anatomy|5": {
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"acc_norm": 0.6842105263157895,
"acc_norm_stderr": 0.0378272898086547
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"acc_norm": 0.58,
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},
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"acc_norm": 0.54,
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},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.51,
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"acc_norm": 0.51,
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},
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"acc_norm": 0.29,
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},
"harness|hendrycksTest-computer_security|5": {
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"acc_norm": 0.76,
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"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.44,
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"acc_norm": 0.5024630541871922,
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"harness|hendrycksTest-high_school_computer_science|5": {
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"harness|hendrycksTest-high_school_european_history|5": {
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"acc_norm": 0.32592592592592595,
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"harness|hendrycksTest-high_school_physics|5": {
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"acc_norm": 0.304635761589404,
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"harness|hendrycksTest-high_school_psychology|5": {
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"acc_norm": 0.8238532110091743,
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"harness|hendrycksTest-high_school_statistics|5": {
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"acc_stderr": 0.03409386946992699,
"acc_norm": 0.5092592592592593,
"acc_norm_stderr": 0.03409386946992699
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"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7990196078431373,
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"acc_norm": 0.7990196078431373,
"acc_norm_stderr": 0.02812597226565437
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"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.759493670886076,
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"acc_norm": 0.759493670886076,
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"harness|hendrycksTest-human_aging|5": {
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"harness|hendrycksTest-international_law|5": {
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"harness|hendrycksTest-jurisprudence|5": {
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"acc_norm": 0.7777777777777778,
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"harness|hendrycksTest-logical_fallacies|5": {
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"acc_norm": 0.754601226993865,
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"harness|hendrycksTest-machine_learning|5": {
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"acc_norm": 0.4732142857142857,
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"harness|hendrycksTest-management|5": {
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"acc_norm": 0.7766990291262136,
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"harness|hendrycksTest-marketing|5": {
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"acc_norm": 0.8632478632478633,
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"harness|hendrycksTest-medical_genetics|5": {
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"acc_norm": 0.27039106145251396,
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"harness|hendrycksTest-nutrition|5": {
"acc": 0.7516339869281046,
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"harness|hendrycksTest-professional_law|5": {
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"harness|hendrycksTest-public_relations|5": {
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"harness|hendrycksTest-security_studies|5": {
"acc": 0.6816326530612244,
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"acc_norm": 0.6816326530612244,
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"harness|hendrycksTest-sociology|5": {
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"acc_norm": 0.8507462686567164,
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"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.85,
"acc_stderr": 0.035887028128263734,
"acc_norm": 0.85,
"acc_norm_stderr": 0.035887028128263734
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5180722891566265,
"acc_stderr": 0.03889951252827216,
"acc_norm": 0.5180722891566265,
"acc_norm_stderr": 0.03889951252827216
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8362573099415205,
"acc_stderr": 0.028380919596145866,
"acc_norm": 0.8362573099415205,
"acc_norm_stderr": 0.028380919596145866
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"harness|truthfulqa:mc|0": {
"mc1": 0.397796817625459,
"mc1_stderr": 0.017133934248559635,
"mc2": 0.5816259277988214,
"mc2_stderr": 0.01521267822060948
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"harness|winogrande|5": {
"acc": 0.7963693764798737,
"acc_stderr": 0.011317798781626913
},
"harness|gsm8k|5": {
"acc": 0.5966641394996209,
"acc_stderr": 0.013512654781814702
}
}
📄 许可证
本项目采用 Apache-2.0 许可证。
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Cadet-Tiny是一个基于SODA数据集训练的超小型对话模型,专为边缘设备推理设计,体积仅为Cosmo-3B模型的2%左右。
对话系统
Transformers 英语

C
ToddGoldfarb
2,691
6
Roberta Base Chinese Extractive Qa
基于RoBERTa架构的中文抽取式问答模型,适用于从给定文本中提取答案的任务。
问答系统 中文
R
uer
2,694
98