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--- |
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base_model: NousResearch/Llama-2-13b-hf |
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tags: |
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- llama-2 |
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- instruct |
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- finetune |
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- alpaca |
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- gpt4 |
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- synthetic data |
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- distillation |
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datasets: |
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- teknium/openhermes |
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model-index: |
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- name: openhermes-13b |
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results: [] |
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license: mit |
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language: |
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- en |
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--- |
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# OpenHermes-13B |
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## Model description |
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OpenHermes 13B is the first fine tune of the Hermes dataset that has a fully open source dataset! |
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OpenHermes was trained on 242,000 entries of primarily GPT-4 generated data, from open datasets across the AI landscape, including: |
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- GPTeacher - General Instruct, Roleplay v1, Roleplay v2, and Code Instruct Datasets, by Teknium |
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- WizardLM (v1, evol_instruct 70k), by WizardLM Team/nlpxucan |
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- Airoboros GPT-4 (v1.0), by JonDurbin |
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- Camel-AI's domain expert datasets, by the Camel-AI Team |
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- CodeAlpaca, by Sahil2801 |
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- GPT4-LLM and Unnatural Instructions, by Microsoft |
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Filtering included removal of OpenAI refusals, disclaimers, and "As an AI" type examples and more |
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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. |
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The WANDB Project is public and can be examined at this link: https://wandb.ai/teknium1/openhermes/runs/openhermes-v2-fullft-13b |
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Huge thank you to [main_horse](https://twitter.com/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! |
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## Example Outputs |
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## Benchmark Information |
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## Benchmark Results |
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GPT-4All Benchmark Set |
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``` |
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| Task |Version| Metric |Value | |Stderr| |
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|-------------|------:|--------|-----:|---|-----:| |
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|arc_challenge| 0|acc |0.5009|± |0.0146| |
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| | |acc_norm|0.5247|± |0.0146| |
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|arc_easy | 0|acc |0.8127|± |0.0080| |
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| | |acc_norm|0.7854|± |0.0084| |
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|boolq | 1|acc |0.8153|± |0.0068| |
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|hellaswag | 0|acc |0.6126|± |0.0049| |
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| | |acc_norm|0.7995|± |0.0040| |
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|openbookqa | 0|acc |0.3660|± |0.0216| |
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| | |acc_norm|0.4600|± |0.0223| |
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|piqa | 0|acc |0.7922|± |0.0095| |
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| | |acc_norm|0.8112|± |0.0091| |
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|winogrande | 0|acc |0.7293|± |0.0125| |
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Average: 0.7036 |
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``` |
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AGI-Eval |
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``` |
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| Task |Version| Metric |Value | |Stderr| |
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|------------------------------|------:|--------|-----:|---|-----:| |
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|agieval_aqua_rat | 0|acc |0.2008|± |0.0252| |
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| | |acc_norm|0.2126|± |0.0257| |
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|agieval_logiqa_en | 0|acc |0.3410|± |0.0186| |
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| | |acc_norm|0.3564|± |0.0188| |
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|agieval_lsat_ar | 0|acc |0.2261|± |0.0276| |
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| | |acc_norm|0.2174|± |0.0273| |
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|agieval_lsat_lr | 0|acc |0.3725|± |0.0214| |
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| | |acc_norm|0.3373|± |0.0210| |
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|agieval_lsat_rc | 0|acc |0.4684|± |0.0305| |
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| | |acc_norm|0.4572|± |0.0304| |
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|agieval_sat_en | 0|acc |0.6553|± |0.0332| |
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| | |acc_norm|0.5971|± |0.0343| |
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|agieval_sat_en_without_passage| 0|acc |0.4515|± |0.0348| |
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| | |acc_norm|0.4029|± |0.0343| |
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|agieval_sat_math | 0|acc |0.3273|± |0.0317| |
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| | |acc_norm|0.2636|± |0.0298| |
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Average: 0.3556 |
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``` |
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BigBench Reasoning Test |
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``` |
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| Task |Version| Metric |Value | |Stderr| |
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|------------------------------------------------|------:|---------------------|-----:|---|-----:| |
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|bigbench_causal_judgement | 0|multiple_choice_grade|0.5368|± |0.0363| |
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|bigbench_date_understanding | 0|multiple_choice_grade|0.7127|± |0.0236| |
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|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.3023|± |0.0286| |
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|bigbench_geometric_shapes | 0|multiple_choice_grade|0.1003|± |0.0159| |
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| | |exact_str_match |0.0000|± |0.0000| |
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|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.2720|± |0.0199| |
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|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.1986|± |0.0151| |
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|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.4500|± |0.0288| |
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|bigbench_movie_recommendation | 0|multiple_choice_grade|0.2880|± |0.0203| |
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|bigbench_navigate | 0|multiple_choice_grade|0.5000|± |0.0158| |
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|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.5390|± |0.0111| |
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|bigbench_ruin_names | 0|multiple_choice_grade|0.3906|± |0.0231| |
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|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.1844|± |0.0123| |
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|bigbench_snarks | 0|multiple_choice_grade|0.5249|± |0.0372| |
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|bigbench_sports_understanding | 0|multiple_choice_grade|0.5335|± |0.0159| |
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|bigbench_temporal_sequences | 0|multiple_choice_grade|0.2980|± |0.0145| |
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|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2048|± |0.0114| |
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|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1297|± |0.0080| |
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|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.4500|± |0.0288| |
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Average: 36.75 |
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``` |
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This is a slight improvement on GPT4ALL Suite and BigBench Suite, with a degredation in AGIEval compared to the original hermes. |
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Average Score Comparison between Nous-Hermes Llama-2 and OpenHermes Llama-2: |
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``` |
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| Bench | Nous-Hermes | OpenHermes | Change | |
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|------------------------------|------------:|------------|--------| |
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|GPT4All | 70.00| 70.36| +0.36| |
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|------------------------------------------------------------------| |
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|BigBench | 36.57| 36.75| +0.18| |
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|------------------------------------------------------------------| |
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|AGI Eval | 37.20| 35.56| -1.64| |
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``` |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 2 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 300 |
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- num_epochs: 3 |
