Model reference · open weights
OpenHermes is an open-weight language model from teknium. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Maker | teknium |
|---|---|
| Type | Language models |
| Task | Text gen |
| Context | 4k tokens |
| Runs with | transformers |
| Based on | NousResearch/Llama-2-7b-hf |
| Released | 2023-09-14 |
| Popularity | 41 downloads / month |
| Licence | Open weights |
About
OpenHermes 7B is the first fine tune of the Hermes dataset that has a fully open source dataset!
What is unique about this 7B model is that it used sample packing, which speeds up training by many multiples if the dataset token averages arent near the max sequence length.
OpenHermes was trained on 242,000 entries of primarily GPT-4 generated data, from open datasets across the AI landscape, including:
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.
The WANDB Project is public and can be examined at this link: https://wandb.ai/teknium1/openhermes/runs/openhermes-v2-qlora-7b-packed
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!
GPT-4All Benchmark Set
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.4727|± |0.0146|
| | |acc_norm|0.4957|± |0.0146|
|arc_easy | 0|acc |0.7862|± |0.0084|
| | |acc_norm|0.7643|± |0.0087|
|boolq | 1|acc |0.7801|± |0.0072|
|hellaswag | 0|acc |0.5789|± |0.0049|
| | |acc_norm|0.7654|± |0.0042|
|openbookqa | 0|acc |0.3480|± |0.0213|
| | |acc_norm|0.4500|± |0.0223|
|piqa | 0|acc |0.7867|± |0.0096|
| | |acc_norm|0.7938|± |0.0094|
|winogrande | 0|acc |0.7048|± |0.0128|
Average: 0.679
BigBench:
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|0.5000|± |0.0364|
|bigbench_date_understanding | 0|multiple_choice_grade|0.5908|± |0.0256|
|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|0.2520|± |0.0194|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.1871|± |0.0148|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.3833|± |0.0281|
|bigbench_movie_recommendation | 0|multiple_choice_grade|0.2500|± |0.0194|
|bigbench_navigate | 0|multiple_choice_grade|0.5000|± |0.0158|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.4370|± |0.0111|
|bigbench_ruin_names | 0|multiple_choice_grade|0.2679|± |0.0209|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.2495|± |0.0137|
|bigbench_snarks | 0|multiple_choice_grade|0.5249|± |0.0372|
|bigbench_sports_understanding | 0|multiple_choice_grade|0.5406|± |0.0159|
|bigbench_temporal_sequences | 0|multiple_choice_grade|0.2470|± |0.0136|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.1944|± |0.0112|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1509|± |0.0086|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.3833|± |0.0281|
Average: 0.3367
AGI Eval
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.2441|± |0.0270|
| | |acc_norm|0.2402|± |0.0269|
|agieval_logiqa_en | 0|acc |0.2458|± |0.0169|
| | |acc_norm|0.2965|± |0.0179|
|agieval_lsat_ar | 0|acc |0.2522|± |0.0287|
| | |acc_norm|0.2130|± |0.0271|
|agieval_lsat_lr | 0|acc |0.2745|± |0.0198|
| | |acc_norm|0.2686|± |0.0196|
|agieval_lsat_rc | 0|acc |0.2900|± |0.0277|
| | |acc_norm|0.2379|± |0.0260|
|agieval_sat_en | 0|acc |0.4466|± |0.0347|
| | |acc_norm|0.3738|± |0.0338|
|agieval_sat_en_without_passage| 0|acc |0.3738|± |0.0338|
| | |acc_norm|0.3301|± |0.0328|
|agieval_sat_math | 0|acc |0.2318|± |0.0285|
| | |acc_norm|0.1864|± |0.0263|
Average: 0.2683
TruthfulQA:
hf-causal-experimental (pretrained=teknium/OpenHermes-7B,dtype=float16), limit: None, provide_description: False, num_fewshot: 0, batch_size: 8
| Task |Version|Metric|Value | |Stderr|
|-------------|------:|------|-----:|---|-----:|
|truthfulqa_mc| 1|mc2 |0.4542|± |0.0148|
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys openhermes for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (openhermes below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"openhermes","messages":[{"role":"user","content":"Hello"}]}'
Create an account — your API key is available in the console. 5M tokens/month currently included with every new account at launch.