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---
license: apache-2.0
datasets:
- Minami-su/Amara-o2-dataset
---
<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
```python
# 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
<div align="left">
| 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.
</div> |