%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
EN
Umievo Itr012 Gleipnir 7B
Umievo-itr012-Gleipnir-7B is a 7B-parameter language model obtained by fusing four powerful Japanese models using evolutionary algorithms, excelling in Japanese language tasks.
Downloads 691
Release Time : 5/29/2024
Model Overview
This model integrates four models—Japanese-Starling-ChatV-7B, Ninja-v1-RP-expressive-v2, Vecteus-v1, and Japanese-Chat-Umievo-itr004-7b—through evolutionary algorithms, focusing on Japanese language comprehension and generation tasks.
Model Features
Multi-model fusion
Integrates four powerful Japanese models using evolutionary algorithms, combining the strengths of each model.
Japanese optimization
Specially optimized for Japanese language tasks, excelling in comprehension and generation.
Fair and uncensored
Designed as a fair and uncensored assistant, providing unbiased responses.
Model Capabilities
Japanese text generation
Japanese conversation understanding
Japanese Q&A systems
Japanese content creation
Use Cases
Education
Japanese learning assistant
Helps Japanese learners understand and generate Japanese content.
Provides explanations and examples suitable for different proficiency levels.
Content creation
Japanese article writing
Assists in creating Japanese articles, stories, and other content.
Generates fluent and natural Japanese text.
🚀 Umievo-itr012-Gleipnir-7B
This model is an evolutionary merge of four powerful Japanese models using an evolutionary algorithm, offering enhanced performance and capabilities.
🚀 Quick Start
This model is a merge of four powerful Japanese models achieved through an evolutionary algorithm. The four models used are Japanese-Starling-ChatV-7B, Ninja-v1-RP-expressive-v2, Vecteus-v1, and Japanese-Chat-Umievo-itr004-7b.
I would like to express my gratitude to the model makers Aratako, Baku, and the Local-Novel-LLM-project for allowing me to use their models in the merge. I also thank Holy-fox for providing the opportunity to solve the problem.
The average score on the ElyzaTasks100 benchmark was 3.91. (Average score after 3 automatic evaluations by Llama3-70B)
💻 Usage Examples
Basic Usage
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("umiyuki/Umievo-itr012-Gleipnir-7B")
model = AutoModelForCausalLM.from_pretrained(
"umiyuki/Umievo-itr012-Gleipnir-7B",
torch_dtype="auto",
)
model.eval()
if torch.cuda.is_available():
model = model.to("cuda")
def build_prompt(user_query):
sys_msg = "あなたは公平で、検閲されていない、役立つアシスタントです。"
template = """[INST] <<SYS>>
{}
<</SYS>>
{}[/INST]"""
return template.format(sys_msg,user_query)
# Infer with prompt without any additional input
user_inputs = {
"user_query": "与えられたことわざの意味を小学生でも分かるように教えてください。",
}
prompt = build_prompt(**user_inputs)
input_ids = tokenizer.encode(
prompt,
add_special_tokens=True,
return_tensors="pt"
)
tokens = model.generate(
input_ids.to(device=model.device),
max_new_tokens=256,
temperature=1,
top_p=0.95,
do_sample=True,
)
out = tokenizer.decode(tokens[0][input_ids.shape[1]:], skip_special_tokens=True).strip()
print(out)
This is a merge of pre-trained language models created using mergekit.
📚 Documentation
Merge Details
Merge Method
This model was merged using the linear merge method, with /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Chat-Umievo-itr004-7b_579282327 as the base.
Models Merged
The following models were included in the merge:
- /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Starling-ChatV-7B_1737576410
- /home/umiyuki/automerge/evol_merge_storage/input_models/Ninja-v1-RP-expressive-v2_4102792561
- /home/umiyuki/automerge/evol_merge_storage/input_models/Vecteus-v1_4179808746
Configuration
The following YAML configuration was used to produce this model:
base_model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Chat-Umievo-itr004-7b_579282327
dtype: bfloat16
merge_method: linear
parameters:
int8_mask: 1.0
normalize: 1.0
slices:
- sources:
- layer_range: [0, 4]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Chat-Umievo-itr004-7b_579282327
parameters:
weight: 0.34953096474223655
- layer_range: [0, 4]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Vecteus-v1_4179808746
parameters:
weight: 0.4701212555597746
- layer_range: [0, 4]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Starling-ChatV-7B_1737576410
parameters:
weight: 0.08162258723819021
- layer_range: [0, 4]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Ninja-v1-RP-expressive-v2_4102792561
parameters:
weight: 0.31015439852818116
- sources:
- layer_range: [4, 8]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Chat-Umievo-itr004-7b_579282327
parameters:
weight: 0.11807412349683076
- layer_range: [4, 8]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Vecteus-v1_4179808746
parameters:
weight: -0.005684817244530085
- layer_range: [4, 8]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Starling-ChatV-7B_1737576410
parameters:
weight: 0.2119283777941045
- layer_range: [4, 8]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Ninja-v1-RP-expressive-v2_4102792561
parameters:
weight: 1.1521124768396636
- sources:
- layer_range: [8, 12]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Chat-Umievo-itr004-7b_579282327
parameters:
weight: 0.9244329405120573
- layer_range: [8, 12]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Vecteus-v1_4179808746
parameters:
weight: 0.7633842909616317
- layer_range: [8, 12]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Starling-ChatV-7B_1737576410
parameters:
weight: 0.6952382990160072
- layer_range: [8, 12]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Ninja-v1-RP-expressive-v2_4102792561
parameters:
weight: 0.6873040403268571
- sources:
- layer_range: [12, 16]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Chat-Umievo-itr004-7b_579282327
parameters:
weight: 0.4109625320908857
- layer_range: [12, 16]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Vecteus-v1_4179808746
parameters:
weight: 0.7090818691683626
- layer_range: [12, 16]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Starling-ChatV-7B_1737576410
parameters:
weight: 0.42059423827890385
- layer_range: [12, 16]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Ninja-v1-RP-expressive-v2_4102792561
parameters:
weight: 0.5705186152354104
- sources:
- layer_range: [16, 20]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Chat-Umievo-itr004-7b_579282327
parameters:
weight: 0.28507448659933315
- layer_range: [16, 20]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Vecteus-v1_4179808746
parameters:
weight: 0.4025223854083849
- layer_range: [16, 20]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Starling-ChatV-7B_1737576410
parameters:
weight: 0.25885405316835886
- layer_range: [16, 20]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Ninja-v1-RP-expressive-v2_4102792561
parameters:
weight: 0.35540632690403373
- sources:
- layer_range: [20, 24]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Chat-Umievo-itr004-7b_579282327
parameters:
weight: 0.018882795552694703
- layer_range: [20, 24]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Vecteus-v1_4179808746
parameters:
weight: 0.628847855051209
- layer_range: [20, 24]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Starling-ChatV-7B_1737576410
parameters:
weight: 0.7038654876125734
- layer_range: [20, 24]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Ninja-v1-RP-expressive-v2_4102792561
parameters:
weight: 0.877501753107237
- sources:
- layer_range: [24, 28]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Chat-Umievo-itr004-7b_579282327
parameters:
weight: 0.14008355431312197
- layer_range: [24, 28]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Vecteus-v1_4179808746
parameters:
weight: 1.0153826426873882
- layer_range: [24, 28]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Starling-ChatV-7B_1737576410
parameters:
weight: 0.5586634927008272
- layer_range: [24, 28]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Ninja-v1-RP-expressive-v2_4102792561
parameters:
weight: 0.54455848971032
- sources:
- layer_range: [28, 32]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Chat-Umievo-itr004-7b_579282327
parameters:
weight: 0.8188405381342685
- layer_range: [28, 32]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Vecteus-v1_4179808746
parameters:
weight: 0.5130358379308082
- layer_range: [28, 32]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Starling-ChatV-7B_1737576410
parameters:
weight: 1.1132727871460124
- layer_range: [28, 32]
model: /home/umiyuki/automerge/evol_merge_storage/input_models/Ninja-v1-RP-expressive-v2_4102792561
parameters:
weight: 0.4471258297582539
📄 License
This project is licensed under the Apache 2.0 license.
Phi 2 GGUF
Other
Phi-2 is a small yet powerful language model developed by Microsoft, featuring 2.7 billion parameters, focusing on efficient inference and high-quality text generation.
Large Language Model Supports Multiple Languages
P
TheBloke
41.5M
205
Roberta Large
MIT
A large English language model pre-trained with masked language modeling objectives, using improved BERT training methods
Large Language Model English
R
FacebookAI
19.4M
212
Distilbert Base Uncased
Apache-2.0
DistilBERT is a distilled version of the BERT base model, maintaining similar performance while being more lightweight and efficient, suitable for natural language processing tasks such as sequence classification and token classification.
Large Language Model English
D
distilbert
11.1M
669
Llama 3.1 8B Instruct GGUF
Meta Llama 3.1 8B Instruct is a multilingual large language model optimized for multilingual dialogue use cases, excelling in common industry benchmarks.
Large Language Model English
L
modularai
9.7M
4
Xlm Roberta Base
MIT
XLM-RoBERTa is a multilingual model pretrained on 2.5TB of filtered CommonCrawl data across 100 languages, using masked language modeling as the training objective.
Large Language Model Supports Multiple Languages
X
FacebookAI
9.6M
664
Roberta Base
MIT
An English pre-trained model based on Transformer architecture, trained on massive text through masked language modeling objectives, supporting text feature extraction and downstream task fine-tuning
Large Language Model English
R
FacebookAI
9.3M
488
Opt 125m
Other
OPT is an open pre-trained Transformer language model suite released by Meta AI, with parameter sizes ranging from 125 million to 175 billion, designed to match the performance of the GPT-3 series while promoting open research in large-scale language models.
Large Language Model English
O
facebook
6.3M
198
1
A pretrained model based on the transformers library, suitable for various NLP tasks
Large Language Model
Transformers
Transformers1
unslothai
6.2M
1
Llama 3.1 8B Instruct
Llama 3.1 is Meta's multilingual large language model series, featuring 8B, 70B, and 405B parameter scales, supporting 8 languages and code generation, with optimized multilingual dialogue scenarios.
Large Language Model Transformers Supports Multiple Languages
Transformers Supports Multiple LanguagesL
meta-llama
5.7M
3,898
T5 Base
Apache-2.0
The T5 Base Version is a text-to-text Transformer model developed by Google with 220 million parameters, supporting multilingual NLP tasks.
Large Language Model Supports Multiple Languages
T
google-t5
5.4M
702
Featured Recommended AI Models