metadata
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.
💻 Usage
# 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
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 | 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