---
title: Smaug-Qwen2-72B-Instruct
canonical_url: "https://www.modelscope.cn/models/QwenCollection/Smaug-Qwen2-72B-Instruct"
md_url: "https://www.modelscope.cn/models/QwenCollection/Smaug-Qwen2-72B-Instruct.md"
repository: QwenCollection/Smaug-Qwen2-72B-Instruct
last_updated: 2024-11-21
license: other
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - qwen2
architectures:
  - Qwen2ForCausalLM
parameters: 72.7B
tensor_type:
  - BF16
library_name:
  - pytorch
  - safetensors
frameworks:
  - pytorch
language:
  - en
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 2221
stars: 0
tags:
  - chat
---

# Smaug-Qwen2-72B-Instruct

> Smaug-Qwen2-72B-Instruct - QwenCollection 在 ModelScope 开源的模型。Smaug-Qwen2-72B-Instruct

QwenCollection/Smaug-Qwen2-72B-Instruct 是 ModelScope 魔搭社区上的 72.7B 参数text-generation模型，采用 other 许可，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: QwenCollection/Smaug-Qwen2-72B-Instruct
- **License**: other
- **Tasks**: text-generation
- **Parameters**: 72.7B
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Tags**: chat
- **Downloads**: 2221
- **Stars**: 0
- **Last updated**: 2024-11-21

Source: https://www.modelscope.cn/models/QwenCollection/Smaug-Qwen2-72B-Instruct

---

# Smaug-Qwen2-72B-Instruct

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/NtH_6eS-yyuEgbKeiek1_.png)

# Introduction

We introduce the latest in the Smaug series - a finetune of [Qwen2-72B-Instruct](https://huggingface.co/Qwen/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:

```python
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](https://github.com/EleutherAI/lm-evaluation-harness). See [this PR](https://github.com/EleutherAI/lm-evaluation-harness/pull/2013).

#### 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](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_abacusai__Smaug-Qwen2-72B-Instruct)

|      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|