File size: 8,779 Bytes
347a75c 8ce3503 347a75c 96a578b 347a75c 96a578b 347a75c 96a578b 347a75c 96a578b 347a75c 96a578b 347a75c 96a578b 347a75c 96a578b 347a75c 96a578b 347a75c 96a578b 347a75c dd945a5 b6284ac 8ce3503 |
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 |
---
library_name: transformers
tags:
- orpo
- llama3-8B
- Supervised_Training
model-index:
- name: LLAMA_Harsha_8_B_ORDP_10k
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: 34.64
name: strict accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=asharsha30/LLAMA_Harsha_8_B_ORDP_10k
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: 25.73
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=asharsha30/LLAMA_Harsha_8_B_ORDP_10k
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: 5.21
name: exact match
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=asharsha30/LLAMA_Harsha_8_B_ORDP_10k
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: 3.13
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=asharsha30/LLAMA_Harsha_8_B_ORDP_10k
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: 7.07
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=asharsha30/LLAMA_Harsha_8_B_ORDP_10k
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: 20.11
name: accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=asharsha30/LLAMA_Harsha_8_B_ORDP_10k
name: Open LLM Leaderboard
license: apache-2.0
datasets:
- mlabonne/orpo-dpo-mix-40k
language:
- en
base_model:
- meta-llama/Llama-3.1-8B
---
# asharsha30/LLAMA_Harsha_8_B_ORDP_10k
This model is the fine tune of NousResearch/Meta-Llama-3-8B using the 12,000 steps of [mlabonne/orpo-dpo-mix-40k](https://huggingface.co/datasets/mlabonne/orpo-dpo-mix-40k).
## 💻 Usage
```python
# Use a pipeline as a high-level helper
from transformers import pipeline
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="asharsha30/LLAMA_Harsha_8_B_ORDP_10k")
pipe(messages)
```
## 📈Training And Evaluation Report:
Reports from Wandb
https://wandb.ai/asharshavardhana96-texas-a-m-university/huggingface/runs/gky6j4vn?nw=nwuserasharshavardhana96
## Acknowledgment:
Huge thanks to Maxime Labonne for his brilliant blog post covering about the techniques related to finetuning the llama models using SFT and ORPO
## Evaluated Using:
The model is evaluated using the https://github.com/mlabonne/llm-autoeval and the results are summarized from the generated gist https://gist.github.com/asharsha30-1996/4162fc98d9669aab3080645c54905bd0
## Accuracy measure on Neous Benchmarks:
| Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|----------------------------------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[LLAMA_Harsha_8_B_ORDP_10k](https://huggingface.co/asharsha30/LLAMA_Harsha_8_B_ORDP_10k)| 35.54| 71.15| 55.39| 37.96| 50.01|
### AGIEval
| Task |Version| Metric |Value| |Stderr|
|------------------------------|------:|--------|----:|---|-----:|
|agieval_aqua_rat | 0|acc |26.77|± | 2.78|
| | |acc_norm|27.17|± | 2.80|
|agieval_logiqa_en | 0|acc |31.34|± | 1.82|
| | |acc_norm|33.03|± | 1.84|
|agieval_lsat_ar | 0|acc |18.70|± | 2.58|
| | |acc_norm|19.57|± | 2.62|
|agieval_lsat_lr | 0|acc |42.94|± | 2.19|
| | |acc_norm|35.10|± | 2.12|
|agieval_lsat_rc | 0|acc |52.42|± | 3.05|
| | |acc_norm|43.87|± | 3.03|
|agieval_sat_en | 0|acc |65.53|± | 3.32|
| | |acc_norm|54.37|± | 3.48|
|agieval_sat_en_without_passage| 0|acc |41.75|± | 3.44|
| | |acc_norm|33.98|± | 3.31|
|agieval_sat_math | 0|acc |42.27|± | 3.34|
| | |acc_norm|37.27|± | 3.27|
Average: 35.54%
### GPT4All
| Task |Version| Metric |Value| |Stderr|
|-------------|------:|--------|----:|---|-----:|
|arc_challenge| 0|acc |49.91|± | 1.46|
| | |acc_norm|54.10|± | 1.46|
|arc_easy | 0|acc |80.47|± | 0.81|
| | |acc_norm|80.05|± | 0.82|
|boolq | 1|acc |82.08|± | 0.67|
|hellaswag | 0|acc |61.08|± | 0.49|
| | |acc_norm|80.26|± | 0.40|
|openbookqa | 0|acc |34.00|± | 2.12|
| | |acc_norm|45.00|± | 2.23|
|piqa | 0|acc |79.71|± | 0.94|
| | |acc_norm|81.61|± | 0.90|
|winogrande | 0|acc |74.98|± | 1.22|
Average: 71.15%
### TruthfulQA
| Task |Version|Metric|Value| |Stderr|
|-------------|------:|------|----:|---|-----:|
|truthfulqa_mc| 1|mc1 |37.45|± | 1.69|
| | |mc2 |55.39|± | 1.50|
Average: 55.39%
### Bigbench
| Task |Version| Metric |Value| |Stderr|
|------------------------------------------------|------:|---------------------|----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|57.37|± | 3.60|
|bigbench_date_understanding | 0|multiple_choice_grade|68.02|± | 2.43|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|31.01|± | 2.89|
|bigbench_geometric_shapes | 0|multiple_choice_grade|20.89|± | 2.15|
| | |exact_str_match | 0.00|± | 0.00|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|28.40|± | 2.02|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|20.71|± | 1.53|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|48.67|± | 2.89|
|bigbench_movie_recommendation | 0|multiple_choice_grade|31.60|± | 2.08|
|bigbench_navigate | 0|multiple_choice_grade|50.60|± | 1.58|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|63.25|± | 1.08|
|bigbench_ruin_names | 0|multiple_choice_grade|34.38|± | 2.25|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|21.84|± | 1.31|
|bigbench_snarks | 0|multiple_choice_grade|44.20|± | 3.70|
|bigbench_sports_understanding | 0|multiple_choice_grade|50.30|± | 1.59|
|bigbench_temporal_sequences | 0|multiple_choice_grade|26.30|± | 1.39|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|21.36|± | 1.16|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|15.77|± | 0.87|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|48.67|± | 2.89|
Average: 37.96%
Average score: 50.01%
Elapsed time: 02:36:38 |