Amara-o2-7B-Qwen

<div align="left"> <img src="https://cdn-uploads.huggingface.co/production/uploads/62d7f90b102d144db4b4245b/WIGeEeS5gBvATkSf2GSB-.png" alt="Model Illustration" style="width:50%; max-width:none;" /> </div> <blockquote> “何が綴られていたのか、私たちの文明では到底理解できない” <br/> （所阐述的内容超出了我们文明的理解范围） <br/> — sasakure.UK </blockquote>

How to use

迭代基于Amara-o1-7B-Qwen

# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")

Open Ended Generation Evaluation

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Note: English open-ended conversation evaluations. For AlpacaEval 2.0, we use the length-controlled win rate as the metric.

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