Smaug-Qwen2-72B-Instruct

• Quantization from fp32
• Using i-matrix calibration_datav3.txt

Introduction

We introduce the latest in the Smaug series - a finetune of Qwen2-72B-Instruct

Compared to Qwen2-72B-Instruct, Smaug has better BBH, LiveCodeBench, and Arena-Hard scores (see evaluation results below).

How to use

The prompt format is unchanged from Qwen2-72B-Instruct.

Use with transformers

See the snippet below for usage with Transformers:

import transformers
import torch

model_id = "abacusai/Smaug-Qwen2-72B-Instruct"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]

prompt = pipeline.tokenizer.apply_chat_template(
		messages, 
		tokenize=False, 
		add_generation_prompt=True
)

terminators = [
    pipeline.tokenizer.eos_token_id,
    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = pipeline(
    prompt,
    max_new_tokens=256,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])

Evaluation Results

Big-Bench Hard (BBH)

Note: These results are with corrected parsing for BBH from Eleuther’s lm-evaluation-harness. See this PR.

Overall:

Breakdown:

Smaug-Qwen2-72B-Instruct:

Qwen2-72B-Instruct:

LiveCodeBench

Arena-Hard

Score vs selected others (sourced from: (https://lmsys.org/blog/2024-04-19-arena-hard/#full-leaderboard-with-gpt-4-turbo-as-judge)). GPT-4o and Gemini-1.5-pro-latest were missing from the original blob post, and we produced those numbers from a local run using the same methodology.

MT-Bench

First turn

Second turn

Average