OpenHermes-13B
image/png Model description
OpenHermes 13B is the first fine tune of the Hermes dataset that has a fully open source dataset!
OpenHermes was trained on 242,000 entries of primarily GPT-4 generated data, from open datasets across the AI landscape, including:
GPTeacher - General Instruct, Roleplay v1, Roleplay v2, and Code Instruct Datasets, by Teknium
WizardLM (v1, evol_instruct 70k), by WizardLM Team/nlpxucan
Airoboros GPT-4 (v1.0), by JonDurbin
Camel-AI's domain expert datasets, by the Camel-AI Team
CodeAlpaca, by Sahil2801
GPT4-LLM and Unnatural Instructions, by Microsoft
Filtering included removal of OpenAI refusals, disclaimers, and "As an AI" type examples and more
The base dataset mix the model was trained on is identical to Nous-Hermes', minus the Nous-Instruct and PDACTL datasets which were private datasets.
Huge thank you to
main_horse for compute access and a16z for sponsoring my work, and all the dataset creators and other people who's work has contributed to this project!
Example Outputs
image/png image/png image/png image/png Benchmark Information
Benchmark Results
GPT-4All Benchmark Set
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.5009|± |0.0146|
| | |acc_norm|0.5247|± |0.0146|
|arc_easy | 0|acc |0.8127|± |0.0080|
| | |acc_norm|0.7854|± |0.0084|
|boolq | 1|acc |0.8153|± |0.0068|
|hellaswag | 0|acc |0.6126|± |0.0049|
| | |acc_norm|0.7995|± |0.0040|
|openbookqa | 0|acc |0.3660|± |0.0216|
| | |acc_norm|0.4600|± |0.0223|
|piqa | 0|acc |0.7922|± |0.0095|
| | |acc_norm|0.8112|± |0.0091|
|winogrande | 0|acc |0.7293|± |0.0125|
Average: 0.7036AGI-Eval
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.2008|± |0.0252|
| | |acc_norm|0.2126|± |0.0257|
|agieval_logiqa_en | 0|acc |0.3410|± |0.0186|
| | |acc_norm|0.3564|± |0.0188|
|agieval_lsat_ar | 0|acc |0.2261|± |0.0276|
| | |acc_norm|0.2174|± |0.0273|
|agieval_lsat_lr | 0|acc |0.3725|± |0.0214|
| | |acc_norm|0.3373|± |0.0210|
|agieval_lsat_rc | 0|acc |0.4684|± |0.0305|
| | |acc_norm|0.4572|± |0.0304|
|agieval_sat_en | 0|acc |0.6553|± |0.0332|
| | |acc_norm|0.5971|± |0.0343|
|agieval_sat_en_without_passage| 0|acc |0.4515|± |0.0348|
| | |acc_norm|0.4029|± |0.0343|
|agieval_sat_math | 0|acc |0.3273|± |0.0317|
| | |acc_norm|0.2636|± |0.0298|
Average: 0.3556BigBench Reasoning Test
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|0.5368|± |0.0363|
|bigbench_date_understanding | 0|multiple_choice_grade|0.7127|± |0.0236|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.3023|± |0.0286|
|bigbench_geometric_shapes | 0|multiple_choice_grade|0.1003|± |0.0159|
| | |exact_str_match |0.0000|± |0.0000|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.2720|± |0.0199|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.1986|± |0.0151|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.4500|± |0.0288|
|bigbench_movie_recommendation | 0|multiple_choice_grade|0.2880|± |0.0203|
|bigbench_navigate | 0|multiple_choice_grade|0.5000|± |0.0158|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.5390|± |0.0111|
|bigbench_ruin_names | 0|multiple_choice_grade|0.3906|± |0.0231|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.1844|± |0.0123|
|bigbench_snarks | 0|multiple_choice_grade|0.5249|± |0.0372|
|bigbench_sports_understanding | 0|multiple_choice_grade|0.5335|± |0.0159|
|bigbench_temporal_sequences | 0|multiple_choice_grade|0.2980|± |0.0145|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2048|± |0.0114|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1297|± |0.0080|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.4500|± |0.0288|
Average: 36.75This is a slight improvement on GPT4ALL Suite and BigBench Suite, with a degredation in AGIEval compared to the original hermes.
Average Score Comparison between Nous-Hermes Llama-2 and OpenHermes Llama-2:
| Bench | Nous-Hermes | OpenHermes | Change |
|------------------------------|------------:|------------|--------|
|GPT4All | 70.00| 70.36| +0.36|
|------------------------------------------------------------------|
|BigBench | 36.57| 36.75| +0.18|
|------------------------------------------------------------------|
|AGI Eval | 37.20| 35.56| -1.64|Training procedure
image/png Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 2e-05
train_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 8
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 300
num_epochs: 3
