File size: 2,737 Bytes
17e74ff 4323373 17e74ff 4323373 15f4e1b 4323373 17e74ff 4323373 17e74ff 4244167 17e74ff 4244167 17e74ff 4244167 17e74ff 4244167 17e74ff 4244167 17e74ff 4244167 17e74ff d7193d2 17e74ff 4244167 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
---
license: other
language:
- en
library_name: transformers
tags:
- RLHF
- Nexusflow
- Athene
- Chat Model
---
# Athene-V2-Chat-72B: Rivaling GPT-4o across Benchmarks
<p align="center">
<a href="https://huggingface.co/Nexusflow" target="_blank">Nexusflow HF</a> - <a href="https://discord.gg/HDSVmNAs3y" target="_blank">Nexusflow Discord</a>
</p>
We introduce Athene-V2-Chat-72B, an open-weights LLM that rivals GPT-4o across benchmarks. It is trained through RLHF based off Qwen-2.5-72B.
Athene-V2-Chat-72B excels in chat, math and coding. Its sister model, [Athene-V2-Agent-72B](https://huggingface.co/Nexusflow/Athene-V2-Chat), surpasses GPT-4o in complex function calling and agent applications.
Benchmark performance:
<p align="center" width="100%">
<a><img src="benchmark.jpg" alt="Benchmark" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
</p>
- **Developed by:** The Nexusflow Team
- **Model type:** Chat Model
- **Finetuned from model:** [Qwen 2.5 72B](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct)
- **License**: [Nexusflow Research License](https://huggingface.co/Nexusflow/Athene-V2-Chat/blob/main/Nexusflow_Research_License.pdf)
- **Blog**: https://nexusflow.ai/blogs/athene-V2
## Usage
Athene-V2-Chat uses the same chat template as Qwen 2.5 72B. Below is an example simple usage using the Transformers library.
```Python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Nexusflow/Athene-V2-Chat"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
We found that by adding system prompts that enforce the model to think step by step, the model can do even better in math and problems like counting `r`s in strawberry. For fairness consideration we **do not** include such system prompt during chat evaluation.
## Acknowledgment
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.
|