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---
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license: other
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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library_name: transformers
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tags:
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- RLHF
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- Nexusflow
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- Athene
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- Chat Model
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base_model:
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- Qwen/Qwen2.5-72B-Instruct
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---
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# Athene-V2-Chat-72B: Rivaling GPT-4o across Benchmarks
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<p align="center">
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<a href="https://huggingface.co/Nexusflow" target="_blank">Nexusflow HF</a> - <a href="https://discord.gg/HDSVmNAs3y" target="_blank">Nexusflow Discord</a> - <a href="https://nexusflow.ai/blogs/athene-v2" target="_blank">Athene-V2 Blogpost</a>
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</p>
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We introduce Athene-V2-Chat-72B, an open-weights LLM on-par with GPT-4o across benchmarks. It is trained through RLHF with Qwen-2.5-72B-Instruct as base model.
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Athene-V2-Chat-72B excels in chat, math, and coding. Its sister model, [Athene-V2-Agent-72B](https://huggingface.co/Nexusflow/Athene-V2-Agent), surpasses GPT-4o in complex function calling and agentic applications.
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<p align="center" width="100%">
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<a><img src="benchmark.png" alt="Benchmark" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
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</p>
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- **Developed by:** The Nexusflow Team
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- **Model type:** Chat Model
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- **Finetuned from model:** [Qwen 2.5 72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct)
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- **License**: [Nexusflow Research License](https://huggingface.co/Nexusflow/Athene-V2-Chat/blob/main/Nexusflow_Research_License_.pdf)
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- **Blog**: https://nexusflow.ai/blogs/athene-v2
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## Usage
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Athene-V2-Chat uses the same chat template as Qwen2.5-72B-Instruct. Below is an example simple usage using the Transformers library.
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```Python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "Nexusflow/Athene-V2-Chat"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Write a Python function to return the nth Fibonacci number in log n runtime."
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messages = [
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=2048
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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```
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Note that by adding a system prompt that encourages the model to think step by step, the model can improve further on difficult math queries and problems like counting `r`s in strawberry. For fairness consideration we **do not** include such system prompt during chat evaluation.
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## Acknowledgment
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We would like to thank the [LMSYS Organization](https://lmsys.org/) for their support of testing the model. We would like to thank Qwen Team and the open source community for their efforts in providing the datasets and base models. |
