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--- |
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license: other |
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language: |
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- en |
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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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- Nexusflow/Athene-V2-Chat |
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--- |
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3bpw exl2 quant of: https://huggingface.co/Nexusflow/Athene-V2-Chat |
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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. |
