Orpo-Llama-3.2-1B-40k

AdamLucek/Orpo-Llama-3.2-1B-40k is an ORPO fine tuned version of meta-llama/Llama-3.2-1B on 1 epoch of mlabonne/orpo-dpo-mix-40k.

Trained for 11 hours on an A100 GPU with this training script

For full model details, refer to the base model page meta-llama/Llama-3.2-1B

Evaluations

In comparsion to AdamLucek/Orpo-Llama-3.2-1B-15k using lm-evaluation-harness.

Benchmark	15k Accuracy	15k Normalized	40k Accuracy	40k Normalized	Notes
AGIEval	22.14%	21.01%	23.57%	23.26%	0-Shot Average across multiple reasoning tasks
GPT4ALL	51.15%	54.38%	51.63%	55.00%	0-Shot Average across all categories
TruthfulQA	42.79%	N/A	42.14%	N/A	MC2 accuracy
MMLU	31.22%	N/A	31.01%	N/A	5-Shot Average across all categories
Winogrande	61.72%	N/A	61.12%	N/A	0-shot evaluation
ARC Challenge	32.94%	36.01%	33.36%	37.63%	0-shot evaluation
ARC Easy	64.52%	60.40%	65.91%	60.90%	0-shot evaluation
BoolQ	50.24%	N/A	52.29%	N/A	0-shot evaluation
PIQA	75.46%	74.37%	75.63%	75.19%	0-shot evaluation
HellaSwag	48.56%	64.71%	48.46%	64.50%	0-shot evaluation

Using this Model

from transformers import AutoTokenizer
import transformers
import torch

# Load Model and Pipeline
model = "AdamLucek/Orpo-Llama-3.2-1B-40k"

pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

# Load Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model)

# Generate Message
messages = [{"role": "user", "content": "What is a language model?"}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=1024, do_sample=True, temperature=0.3, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Training Statistics

OpenLLM Leaderboard Metrics

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
leaderboard	N/A
- leaderboard_bbh	N/A					0.3290
- leaderboard_bbh_boolean_expressions	1	none	3	acc_norm	↑	0.6840	±	0.0295
- leaderboard_bbh_causal_judgement	1	none	3	acc_norm	↑	0.5134	±	0.0366
- leaderboard_bbh_date_understanding	1	none	3	acc_norm	↑	0.1920	±	0.0250
- leaderboard_bbh_disambiguation_qa	1	none	3	acc_norm	↑	0.3880	±	0.0309
- leaderboard_bbh_formal_fallacies	1	none	3	acc_norm	↑	0.4680	±	0.0316
- leaderboard_bbh_geometric_shapes	1	none	3	acc_norm	↑	0.0000	±	0
- leaderboard_bbh_hyperbaton	1	none	3	acc_norm	↑	0.4840	±	0.0317
- leaderboard_bbh_logical_deduction_five_objects	1	none	3	acc_norm	↑	0.2000	±	0.0253
- leaderboard_bbh_logical_deduction_seven_objects	1	none	3	acc_norm	↑	0.1360	±	0.0217
- leaderboard_bbh_logical_deduction_three_objects	1	none	3	acc_norm	↑	0.3440	±	0.0301
- leaderboard_bbh_movie_recommendation	1	none	3	acc_norm	↑	0.2280	±	0.0266
- leaderboard_bbh_navigate	1	none	3	acc_norm	↑	0.4200	±	0.0313
- leaderboard_bbh_object_counting	1	none	3	acc_norm	↑	0.3880	±	0.0309
- leaderboard_bbh_penguins_in_a_table	1	none	3	acc_norm	↑	0.1575	±	0.0303
- leaderboard_bbh_reasoning_about_colored_objects	1	none	3	acc_norm	↑	0.1280	±	0.0212
- leaderboard_bbh_ruin_names	1	none	3	acc_norm	↑	0.2000	±	0.0253
- leaderboard_bbh_salient_translation_error_detection	1	none	3	acc_norm	↑	0.2280	±	0.0266
- leaderboard_bbh_snarks	1	none	3	acc_norm	↑	0.5393	±	0.0375
- leaderboard_bbh_sports_understanding	1	none	3	acc_norm	↑	0.5240	±	0.0316
- leaderboard_bbh_temporal_sequences	1	none	3	acc_norm	↑	0.2000	±	0.0253
- leaderboard_bbh_tracking_shuffled_objects_five_objects	1	none	3	acc_norm	↑	0.1640	±	0.0235
- leaderboard_bbh_tracking_shuffled_objects_seven_objects	1	none	3	acc_norm	↑	0.1400	±	0.0220
- leaderboard_bbh_tracking_shuffled_objects_three_objects	1	none	3	acc_norm	↑	0.3520	±	0.0303
- leaderboard_bbh_web_of_lies	1	none	3	acc_norm	↑	0.4880	±	0.0317
- leaderboard_gpqa	N/A					0.2482
- leaderboard_gpqa_diamond	1	none	0	acc_norm	↑	0.2576	±	0.0312
- leaderboard_gpqa_extended	1	none	0	acc_norm	↑	0.2436	±	0.0184
- leaderboard_gpqa_main	1	none	0	acc_norm	↑	0.2433	±	0.0203
- leaderboard_ifeval	3	none	0	inst_level_loose_acc	↑	0.2962	±	N/A
		none	0	inst_level_strict_acc	↑	0.2842	±	N/A
		none	0	prompt_level_loose_acc	↑	0.1516	±	0.0154
		none	0	prompt_level_strict_acc	↑	0.1386	±	0.0149
- leaderboard_math_hard	N/A
- leaderboard_math_algebra_hard	2	none	4	exact_match	↑	0.0000	±	0
- leaderboard_math_counting_and_prob_hard	2	none	4	exact_match	↑	0.0000	±	0
- leaderboard_math_geometry_hard	2	none	4	exact_match	↑	0.0000	±	0
- leaderboard_math_intermediate_algebra_hard	2	none	4	exact_match	↑	0.0000	±	0
- leaderboard_math_num_theory_hard	2	none	4	exact_match	↑	0.0000	±	0
- leaderboard_math_prealgebra_hard	2	none	4	exact_match	↑	0.0000	±	0
- leaderboard_math_precalculus_hard	2	none	4	exact_match	↑	0.0000	±	0
- leaderboard_mmlu_pro	0.1	none	5	acc	↑	0.1222	±	0.0030
- leaderboard_musr	N/A			avg acc_norm		0.3433
- leaderboard_musr_murder_mysteries	1	none	0	acc_norm	↑	0.5120	±	0.0317
- leaderboard_musr_object_placements	1	none	0	acc_norm	↑	0.2500	±	0.0271
- leaderboard_musr_team_allocation	1	none	0	acc_norm	↑	0.2680	±	0.0281