File size: 15,316 Bytes
c7a9cdd 91f2cea df3d9a1 91f2cea df3d9a1 c7a9cdd af01592 91f2cea 84ccd91 1c49031 a4e1166 1c49031 84ccd91 6deb8e6 e40b6f4 84ccd91 6deb8e6 84ccd91 1c49031 a4e1166 1c49031 bcca8ad 1c49031 e40b6f4 1c49031 e40b6f4 df3d9a1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 |
---
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](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|
|