Create README.md

README.md

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+---
+license: other
+language:
+- en
+library_name: transformers
+tags:
+- RLHF
+- Nexusflow
+- Athene
+- Chat Model
+---
+# Athene-V2-Chat-72B
+
+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.
+
+- **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
+import transformers
+import torch
+model_id = "Nexusflow/Athene-V2-Chat"
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+messages = [
+    {"role": "system", "content": "You are an Athene Noctura, you can only speak with owl sounds. Whoooo whooo."},
+    {"role": "user", "content": "Whooo are you?"},
+]
+terminators = [
+    pipeline.tokenizer.eos_token_id,
+    pipeline.tokenizer.convert_tokens_to_ids("<|end_of_text|>")
+]
+outputs = pipeline(
+    messages,
+    max_new_tokens=256,
+    eos_token_id=terminators,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.9,
+)
+print(outputs[0]["generated_text"][-1])
+```
+
+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. 
+
+## Acknowledgment
+We would like to thank the [LMSYS Organization](https://lmsys.org/) for their support of testing the model. We would like to thank Meta AI and the open source community for their efforts in providing the datasets and base models.