KnutJaegersberg/Athene-V2-Chat-72B-4bit

Athene-V2-Chat-72B: Rivaling GPT-4o across Benchmarks

Nexusflow HF - Nexusflow Discord - Athene-V2 Blogpost

used bnb double quantization

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. Athene-V2-Chat-72B excels in chat, math, and coding. Its sister model, Athene-V2-Agent-72B, surpasses GPT-4o in complex function calling and agentic applications.

• Developed by: The Nexusflow Team
• Model type: Chat Model
• Finetuned from model: Qwen 2.5 72B-Instruct
• License: Nexusflow Research License
• Blog: https://nexusflow.ai/blogs/athene-v2

Usage

Athene-V2-Chat uses the same chat template as Qwen2.5-72B-Instruct. Below is an example simple usage using the Transformers library.

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 = "Write a Python function to return the nth Fibonacci number in log n runtime."

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=2048
)

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]

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 rs in strawberry. For fairness consideration we do not include such system prompt during chat evaluation.

Acknowledgment

We would like to thank the LMSYS Organization 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.