DarwinAI-gemma-3-270m
2
license:apache-2.0
by
openfree
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Quick Summary
병합 정보 - 기본 모델 1: google/gemma-3-270m-it - 기본 모델 2: google/gemma-3-270m - 병합 방법: Evolutionary Algorithm - 최종 정확도: 81.
Training Data Analysis
🟡 Average (4.3/10)
Researched training datasets used by DarwinAI-gemma-3-270m with quality assessment
Specialized For
general
science
multilingual
reasoning
Training Datasets (3)
common crawl
🔴 2.5/10
general
science
Key Strengths
- •Scale and Accessibility: At 9.5+ petabytes, Common Crawl provides unprecedented scale for training d...
- •Diversity: The dataset captures billions of web pages across multiple domains and content types, ena...
- •Comprehensive Coverage: Despite limitations, Common Crawl attempts to represent the broader web acro...
Considerations
- •Biased Coverage: The crawling process prioritizes frequently linked domains, making content from dig...
- •Large-Scale Problematic Content: Contains significant amounts of hate speech, pornography, violent c...
wikipedia
🟡 5/10
science
multilingual
Key Strengths
- •High-Quality Content: Wikipedia articles are subject to community review, fact-checking, and citatio...
- •Multilingual Coverage: Available in 300+ languages, enabling training of models that understand and ...
- •Structured Knowledge: Articles follow consistent formatting with clear sections, allowing models to ...
Considerations
- •Language Inequality: Low-resource language editions have significantly lower quality, fewer articles...
- •Biased Coverage: Reflects biases in contributor demographics; topics related to Western culture and ...
arxiv
🟡 5.5/10
science
reasoning
Key Strengths
- •Scientific Authority: Peer-reviewed content from established repository
- •Domain-Specific: Specialized vocabulary and concepts
- •Mathematical Content: Includes complex equations and notation
Considerations
- •Specialized: Primarily technical and mathematical content
- •English-Heavy: Predominantly English-language papers
Explore our comprehensive training dataset analysis
View All DatasetsCode Examples
병합 정보pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("openfree/DarwinAI-gemma-3-270m")
tokenizer = AutoTokenizer.from_pretrained("openfree/DarwinAI-gemma-3-270m")
# 사용 예시
inputs = tokenizer("안녕하세요", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))병합 정보pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("openfree/DarwinAI-gemma-3-270m")
tokenizer = AutoTokenizer.from_pretrained("openfree/DarwinAI-gemma-3-270m")
# 사용 예시
inputs = tokenizer("안녕하세요", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))병합 정보pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("openfree/DarwinAI-gemma-3-270m")
tokenizer = AutoTokenizer.from_pretrained("openfree/DarwinAI-gemma-3-270m")
# 사용 예시
inputs = tokenizer("안녕하세요", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))병합 정보pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("openfree/DarwinAI-gemma-3-270m")
tokenizer = AutoTokenizer.from_pretrained("openfree/DarwinAI-gemma-3-270m")
# 사용 예시
inputs = tokenizer("안녕하세요", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))병합 정보pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("openfree/DarwinAI-gemma-3-270m")
tokenizer = AutoTokenizer.from_pretrained("openfree/DarwinAI-gemma-3-270m")
# 사용 예시
inputs = tokenizer("안녕하세요", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))병합 정보pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("openfree/DarwinAI-gemma-3-270m")
tokenizer = AutoTokenizer.from_pretrained("openfree/DarwinAI-gemma-3-270m")
# 사용 예시
inputs = tokenizer("안녕하세요", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))병합 정보pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("openfree/DarwinAI-gemma-3-270m")
tokenizer = AutoTokenizer.from_pretrained("openfree/DarwinAI-gemma-3-270m")
# 사용 예시
inputs = tokenizer("안녕하세요", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))병합 정보pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("openfree/DarwinAI-gemma-3-270m")
tokenizer = AutoTokenizer.from_pretrained("openfree/DarwinAI-gemma-3-270m")
# 사용 예시
inputs = tokenizer("안녕하세요", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))병합 정보pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("openfree/DarwinAI-gemma-3-270m")
tokenizer = AutoTokenizer.from_pretrained("openfree/DarwinAI-gemma-3-270m")
# 사용 예시
inputs = tokenizer("안녕하세요", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))병합 정보pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("openfree/DarwinAI-gemma-3-270m")
tokenizer = AutoTokenizer.from_pretrained("openfree/DarwinAI-gemma-3-270m")
# 사용 예시
inputs = tokenizer("안녕하세요", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))병합 정보pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("openfree/DarwinAI-gemma-3-270m")
tokenizer = AutoTokenizer.from_pretrained("openfree/DarwinAI-gemma-3-270m")
# 사용 예시
inputs = tokenizer("안녕하세요", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))병합 정보pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("openfree/DarwinAI-gemma-3-270m")
tokenizer = AutoTokenizer.from_pretrained("openfree/DarwinAI-gemma-3-270m")
# 사용 예시
inputs = tokenizer("안녕하세요", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))병합 정보pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("openfree/DarwinAI-gemma-3-270m")
tokenizer = AutoTokenizer.from_pretrained("openfree/DarwinAI-gemma-3-270m")
# 사용 예시
inputs = tokenizer("안녕하세요", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))병합 정보pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("openfree/DarwinAI-gemma-3-270m")
tokenizer = AutoTokenizer.from_pretrained("openfree/DarwinAI-gemma-3-270m")
# 사용 예시
inputs = tokenizer("안녕하세요", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))병합 정보pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("openfree/DarwinAI-gemma-3-270m")
tokenizer = AutoTokenizer.from_pretrained("openfree/DarwinAI-gemma-3-270m")
# 사용 예시
inputs = tokenizer("안녕하세요", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))병합 정보pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("openfree/DarwinAI-gemma-3-270m")
tokenizer = AutoTokenizer.from_pretrained("openfree/DarwinAI-gemma-3-270m")
# 사용 예시
inputs = tokenizer("안녕하세요", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))병합 정보pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("openfree/DarwinAI-gemma-3-270m")
tokenizer = AutoTokenizer.from_pretrained("openfree/DarwinAI-gemma-3-270m")
# 사용 예시
inputs = tokenizer("안녕하세요", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))병합 정보pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("openfree/DarwinAI-gemma-3-270m")
tokenizer = AutoTokenizer.from_pretrained("openfree/DarwinAI-gemma-3-270m")
# 사용 예시
inputs = tokenizer("안녕하세요", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))병합 정보pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("openfree/DarwinAI-gemma-3-270m")
tokenizer = AutoTokenizer.from_pretrained("openfree/DarwinAI-gemma-3-270m")
# 사용 예시
inputs = tokenizer("안녕하세요", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))Deploy This Model
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