README.md

metadata

language:
  - en
license: other
tags:
  - chat
license_name: tongyi-qianwen
license_link: https://huggingface.co./Qwen/Qwen2-72B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
model-index:
  - name: Smaug-Qwen2-72B-Instruct
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: IFEval (0-Shot)
          type: HuggingFaceH4/ifeval
          args:
            num_few_shot: 0
        metrics:
          - type: inst_level_strict_acc and prompt_level_strict_acc
            value: 78.25
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=abacusai/Smaug-Qwen2-72B-Instruct
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: BBH (3-Shot)
          type: BBH
          args:
            num_few_shot: 3
        metrics:
          - type: acc_norm
            value: 56.27
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=abacusai/Smaug-Qwen2-72B-Instruct
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MATH Lvl 5 (4-Shot)
          type: hendrycks/competition_math
          args:
            num_few_shot: 4
        metrics:
          - type: exact_match
            value: 35.35
            name: exact match
        source:
          url: >-
            https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=abacusai/Smaug-Qwen2-72B-Instruct
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GPQA (0-shot)
          type: Idavidrein/gpqa
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 14.88
            name: acc_norm
        source:
          url: >-
            https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=abacusai/Smaug-Qwen2-72B-Instruct
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MuSR (0-shot)
          type: TAUR-Lab/MuSR
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 15.18
            name: acc_norm
        source:
          url: >-
            https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=abacusai/Smaug-Qwen2-72B-Instruct
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU-PRO (5-shot)
          type: TIGER-Lab/MMLU-Pro
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 46.56
            name: accuracy
        source:
          url: >-
            https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=abacusai/Smaug-Qwen2-72B-Instruct
          name: Open LLM Leaderboard

Smaug-Qwen2-72B-Instruct

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:

Model	Groups	Version	Filter	n-shot	Metric	Value		Stderr
Smaug-Qwen2-72B-Instruct	bbh	N/A	get-answer	3	exact_match	0.8241	±	0.0042
Qwen2-72B-Instruct	bbh	N/A	get-answer	3	exact_match	0.8036	±	0.0044

Breakdown:

Smaug-Qwen2-72B-Instruct:

Tasks	Version	Filter	n-shot	Metric	Value	Stderr
bbh	N/A	get-answer	3	exact_match	0.8241	0.0042
- bbh_cot_fewshot_boolean_expressions	2	get-answer	3	exact_match	0.9640	0.0118
- bbh_cot_fewshot_causal_judgement	2	get-answer	3	exact_match	0.6578	0.0348
- bbh_cot_fewshot_date_understanding	2	get-answer	3	exact_match	0.8360	0.0235
- bbh_cot_fewshot_disambiguation_qa	2	get-answer	3	exact_match	0.8280	0.0239
- bbh_cot_fewshot_dyck_languages	2	get-answer	3	exact_match	0.3360	0.0299
- bbh_cot_fewshot_formal_fallacies	2	get-answer	3	exact_match	0.7120	0.0287
- bbh_cot_fewshot_geometric_shapes	2	get-answer	3	exact_match	0.5320	0.0316
- bbh_cot_fewshot_hyperbaton	2	get-answer	3	exact_match	0.9880	0.0069
- bbh_cot_fewshot_logical_deduction_five_objects	2	get-answer	3	exact_match	0.7680	0.0268
- bbh_cot_fewshot_logical_deduction_seven_objects	2	get-answer	3	exact_match	0.5360	0.0316
- bbh_cot_fewshot_logical_deduction_three_objects	2	get-answer	3	exact_match	0.9720	0.0105
- bbh_cot_fewshot_movie_recommendation	2	get-answer	3	exact_match	0.8000	0.0253
- bbh_cot_fewshot_multistep_arithmetic_two	2	get-answer	3	exact_match	0.9720	0.0105
- bbh_cot_fewshot_navigate	2	get-answer	3	exact_match	0.9640	0.0118
- bbh_cot_fewshot_object_counting	2	get-answer	3	exact_match	0.9200	0.0172
- bbh_cot_fewshot_penguins_in_a_table	2	get-answer	3	exact_match	0.8493	0.0297
- bbh_cot_fewshot_reasoning_about_colored_objects	2	get-answer	3	exact_match	0.7560	0.0272
- bbh_cot_fewshot_ruin_names	2	get-answer	3	exact_match	0.8520	0.0225
- bbh_cot_fewshot_salient_translation_error_detection	2	get-answer	3	exact_match	0.5920	0.0311
- bbh_cot_fewshot_snarks	2	get-answer	3	exact_match	0.9101	0.0215
- bbh_cot_fewshot_sports_understanding	2	get-answer	3	exact_match	0.9440	0.0146
- bbh_cot_fewshot_temporal_sequences	2	get-answer	3	exact_match	1.0000	0.0000
- bbh_cot_fewshot_tracking_shuffled_objects_five_objects	2	get-answer	3	exact_match	0.9800	0.0089
- bbh_cot_fewshot_tracking_shuffled_objects_seven_objects	2	get-answer	3	exact_match	0.9560	0.0130
- bbh_cot_fewshot_tracking_shuffled_objects_three_objects	2	get-answer	3	exact_match	0.9640	0.0118
- bbh_cot_fewshot_web_of_lies	2	get-answer	3	exact_match	1.0000	0.0000
- bbh_cot_fewshot_word_sorting	2	get-answer	3	exact_match	0.6560	0.0301

Qwen2-72B-Instruct:

Tasks	Version	Filter	n-shot	Metric	Value	Stderr
bbh	N/A	get-answer	3	exact_match	0.8036	0.0044
- bbh_cot_fewshot_boolean_expressions	2	get-answer	3	exact_match	0.9640	0.0118
- bbh_cot_fewshot_causal_judgement	2	get-answer	3	exact_match	0.6684	0.0345
- bbh_cot_fewshot_date_understanding	2	get-answer	3	exact_match	0.8000	0.0253
- bbh_cot_fewshot_disambiguation_qa	2	get-answer	3	exact_match	0.8360	0.0235
- bbh_cot_fewshot_dyck_languages	2	get-answer	3	exact_match	0.3040	0.0292
- bbh_cot_fewshot_formal_fallacies	2	get-answer	3	exact_match	0.7480	0.0275
- bbh_cot_fewshot_geometric_shapes	2	get-answer	3	exact_match	0.4960	0.0317
- bbh_cot_fewshot_hyperbaton	2	get-answer	3	exact_match	0.9440	0.0146
- bbh_cot_fewshot_logical_deduction_five_objects	2	get-answer	3	exact_match	0.6800	0.0296
- bbh_cot_fewshot_logical_deduction_seven_objects	2	get-answer	3	exact_match	0.4720	0.0316
- bbh_cot_fewshot_logical_deduction_three_objects	2	get-answer	3	exact_match	0.9200	0.0172
- bbh_cot_fewshot_movie_recommendation	2	get-answer	3	exact_match	0.7800	0.0263
- bbh_cot_fewshot_multistep_arithmetic_two	2	get-answer	3	exact_match	0.9760	0.0097
- bbh_cot_fewshot_navigate	2	get-answer	3	exact_match	0.9520	0.0135
- bbh_cot_fewshot_object_counting	2	get-answer	3	exact_match	0.9480	0.0141
- bbh_cot_fewshot_penguins_in_a_table	2	get-answer	3	exact_match	0.5753	0.0410
- bbh_cot_fewshot_reasoning_about_colored_objects	2	get-answer	3	exact_match	0.8120	0.0248
- bbh_cot_fewshot_ruin_names	2	get-answer	3	exact_match	0.8760	0.0209
- bbh_cot_fewshot_salient_translation_error_detection	2	get-answer	3	exact_match	0.5880	0.0312
- bbh_cot_fewshot_snarks	2	get-answer	3	exact_match	0.8764	0.0247
- bbh_cot_fewshot_sports_understanding	2	get-answer	3	exact_match	0.9080	0.0183
- bbh_cot_fewshot_temporal_sequences	2	get-answer	3	exact_match	0.9960	0.0040
- bbh_cot_fewshot_tracking_shuffled_objects_five_objects	2	get-answer	3	exact_match	0.9160	0.0176
- bbh_cot_fewshot_tracking_shuffled_objects_seven_objects	2	get-answer	3	exact_match	0.9400	0.0151
- bbh_cot_fewshot_tracking_shuffled_objects_three_objects	2	get-answer	3	exact_match	0.9440	0.0146
- bbh_cot_fewshot_web_of_lies	2	get-answer	3	exact_match	1.0000	0.0000
- bbh_cot_fewshot_word_sorting	2	get-answer	3	exact_match	0.6680	0.0298

LiveCodeBench

Model	Pass@1	Easy Pass@1	Medium Pass@1	Hard Pass@1
Smaug-Qwen2-72B-Instruct	0.3357	0.7286	0.1633	0.0000
Qwen2-72B-Instruct	0.3139	0.6810	0.1531	0.0000

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.

Model	Score	95% Confidence Interval	Average Tokens
GPT-4-Turbo-2024-04-09	82.6	(-1.8, 1.6)	662
GPT-4o	78.3	(-2.4, 2.1)	685
Gemini-1.5-pro-latest	72.1	(-2.3, 2.2)	630
Claude-3-Opus-20240229	60.4	(-3.3, 2.4)	541
Smaug-Llama-3-70B-Instruct	56.7	(-2.2, 2.6)	661
GPT-4-0314	50.0	(-0.0, 0.0)	423
Smaug-Qwen2-72B-Instruct	48.0	(-1.8, 2.1)	628
Claude-3-Sonnet-20240229	46.8	(-2.1, 2.2)	552
Qwen2-72B-Instruct	43.5	(-2.6, 2.7)	531
Llama-3-70B-Instruct	41.1	(-2.5, 2.4)	583
GPT-4-0613	37.9	(-2.2, 2.0)	354
Mistral-Large-2402	37.7	(-1.9, 2.6)	400
Mixtral-8x22B-Instruct-v0.1	36.4	(-2.7, 2.9)	430
Qwen1.5-72B-Chat	36.1	(-2.5, 2.2)	474
Command-R-Plus	33.1	(-2.1, 2.2)	541
Mistral-Medium	31.9	(-2.3, 2.4)	485
GPT-3.5-Turbo-0613	24.8	(-1.6, 2.0)	401

MT-Bench

First turn

Model	Turn	Score
Qwen2-72B-Instruct	1	9.18125
Smaug-Qwen2-72B-Instruct	1	9.05625

Second turn

Model	Turn	Score
Qwen2-72B-Instruct	2	8.74684
Smaug-Qwen2-72B-Instruct	2	8.67500

Average

Model	Score
Qwen2-72B-Instruct	8.96541
Smaug-Qwen2-72B-Instruct	8.86563

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	41.08
IFEval (0-Shot)	78.25
BBH (3-Shot)	56.27
MATH Lvl 5 (4-Shot)	35.35
GPQA (0-shot)	14.88
MuSR (0-shot)	15.18
MMLU-PRO (5-shot)	46.56