Minami-su/Amara-o2-7B-Qwen

“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”
（所阐述的内容超出了我们文明的理解范围）
— sasakure.UK

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

Model	Arena-Hard	AlpacaEval 2.0
DeepSeek-V2.5-0905	76.2	50.5
Qwen2.5-72B-Instruct	81.2	49.1
LLaMA-3.1 405B	69.3	40.5
Amara-o1-7B-Qwen	?	42.12
Amara-o2-7B-Qwen	?	51.33
GPT-4o-0513	80.4	51.1
Claude-Sonnet-3.5-1022	85.2	52.0
DeepSeek-V3	85.5	70.0

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