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--- |
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license: llama2 |
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language: |
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- en |
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tags: |
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- llama-2 |
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- self-instruct |
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- distillation |
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- synthetic instruction |
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--- |
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# Model Card: Nous-Hermes-Llama2-13b |
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Compute provided by our project sponsor Redmond AI, thank you! Follow RedmondAI on Twitter @RedmondAI. |
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## Model Description |
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Nous-Hermes-Llama2-13b is a state-of-the-art language model fine-tuned on over 300,000 instructions. This model was fine-tuned by Nous Research, with Teknium and Emozilla leading the fine tuning process and dataset curation, Redmond AI sponsoring the compute, and several other contributors. |
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This Hermes model uses the exact same dataset as Hermes on Llama-1. This is to ensure consistency between the old Hermes and new, for anyone who wanted to keep Hermes as similar to the old one, just more capable. |
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This model stands out for its long responses, lower hallucination rate, and absence of OpenAI censorship mechanisms. The fine-tuning process was performed with a 4096 sequence length on an 8x a100 80GB DGX machine. |
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## Example Outputs: |
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## Model Training |
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The model was trained almost entirely on synthetic GPT-4 outputs. Curating high quality GPT-4 datasets enables incredibly high quality in knowledge, task completion, and style. |
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This includes data from diverse sources such as GPTeacher, the general, roleplay v1&2, code instruct datasets, Nous Instruct & PDACTL (unpublished), and several others, detailed further below |
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## Collaborators |
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The model fine-tuning and the datasets were a collaboration of efforts and resources between Teknium, Karan4D, Emozilla, Huemin Art, and Redmond AI. |
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Special mention goes to @winglian for assisting in some of the training issues. |
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Huge shoutout and acknowledgement is deserved for all the dataset creators who generously share their datasets openly. |
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Among the contributors of datasets: |
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- GPTeacher was made available by Teknium |
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- Wizard LM by nlpxucan |
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- Nous Research Instruct Dataset was provided by Karan4D and HueminArt. |
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- GPT4-LLM and Unnatural Instructions were provided by Microsoft |
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- Airoboros dataset by jondurbin |
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- Camel-AI's domain expert datasets are from Camel-AI |
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- CodeAlpaca dataset by Sahil 2801. |
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If anyone was left out, please open a thread in the community tab. |
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## Prompt Format |
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The model follows the Alpaca prompt format: |
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``` |
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### Instruction: |
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<prompt> |
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### Response: |
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<leave a newline blank for model to respond> |
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``` |
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or |
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``` |
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### Instruction: |
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<prompt> |
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### Input: |
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<additional context> |
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### Response: |
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<leave a newline blank for model to respond> |
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``` |
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## Benchmark Results |
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AGI-Eval |
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``` |
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| Task |Version| Metric |Value | |Stderr| |
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|agieval_aqua_rat | 0|acc |0.2362|± |0.0267| |
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| | |acc_norm|0.2480|± |0.0272| |
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|agieval_logiqa_en | 0|acc |0.3425|± |0.0186| |
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| | |acc_norm|0.3472|± |0.0187| |
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|agieval_lsat_ar | 0|acc |0.2522|± |0.0287| |
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| | |acc_norm|0.2087|± |0.0269| |
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|agieval_lsat_lr | 0|acc |0.3510|± |0.0212| |
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| | |acc_norm|0.3627|± |0.0213| |
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|agieval_lsat_rc | 0|acc |0.4647|± |0.0305| |
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| | |acc_norm|0.4424|± |0.0303| |
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|agieval_sat_en | 0|acc |0.6602|± |0.0331| |
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| | |acc_norm|0.6165|± |0.0340| |
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|agieval_sat_en_without_passage| 0|acc |0.4320|± |0.0346| |
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| | |acc_norm|0.4272|± |0.0345| |
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|agieval_sat_math | 0|acc |0.2909|± |0.0307| |
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| | |acc_norm|0.2727|± |0.0301| |
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``` |
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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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|arc_challenge| 0|acc |0.5102|± |0.0146| |
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| | |acc_norm|0.5213|± |0.0146| |
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|arc_easy | 0|acc |0.7959|± |0.0083| |
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| | |acc_norm|0.7567|± |0.0088| |
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|boolq | 1|acc |0.8394|± |0.0064| |
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|hellaswag | 0|acc |0.6164|± |0.0049| |
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| | |acc_norm|0.8009|± |0.0040| |
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|openbookqa | 0|acc |0.3580|± |0.0215| |
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| | |acc_norm|0.4620|± |0.0223| |
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|piqa | 0|acc |0.7992|± |0.0093| |
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| | |acc_norm|0.8069|± |0.0092| |
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|winogrande | 0|acc |0.7127|± |0.0127| |
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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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|bigbench_causal_judgement | 0|multiple_choice_grade|0.5526|± |0.0362| |
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|bigbench_date_understanding | 0|multiple_choice_grade|0.7344|± |0.0230| |
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|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.2636|± |0.0275| |
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|bigbench_geometric_shapes | 0|multiple_choice_grade|0.0195|± |0.0073| |
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| | |exact_str_match |0.0000|± |0.0000| |
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|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.2760|± |0.0200| |
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|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2100|± |0.0154| |
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|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.4400|± |0.0287| |
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|bigbench_movie_recommendation | 0|multiple_choice_grade|0.2440|± |0.0192| |
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|bigbench_navigate | 0|multiple_choice_grade|0.4950|± |0.0158| |
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|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.5570|± |0.0111| |
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|bigbench_ruin_names | 0|multiple_choice_grade|0.3728|± |0.0229| |
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|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.1854|± |0.0123| |
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|bigbench_snarks | 0|multiple_choice_grade|0.6298|± |0.0360| |
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|bigbench_sports_understanding | 0|multiple_choice_grade|0.6156|± |0.0155| |
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|bigbench_temporal_sequences | 0|multiple_choice_grade|0.3140|± |0.0147| |
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|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2032|± |0.0114| |
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|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1406|± |0.0083| |
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|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.4400|± |0.0287| |
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``` |
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These are the highest benchmarks Hermes has seen on every metric, achieving the following average scores: |
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- GPT4All benchmark average is now 70.0 - from 68.8 in Hermes-Llama1 |
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- 0.3657 on BigBench, up from 0.328 on hermes-llama1 |
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- 0.372 on AGIEval, up from 0.354 on Hermes-llama1 |
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These benchmarks currently have us at #1 on ARC-c, ARC-e, Hellaswag, and OpenBookQA, and 2nd place on Winogrande, comparing to GPT4all's benchmarking list, supplanting Hermes 1 for the new top position. |
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## Resources for Applied Use Cases: |
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For an example of a back and forth chatbot using huggingface transformers and discord, check out: https://github.com/teknium1/alpaca-discord |
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For an example of a roleplaying discord chatbot, check out this: https://github.com/teknium1/alpaca-roleplay-discordbot |
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## Future Plans |
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We plan to continue to iterate on both more high quality data, and new data filtering techniques to eliminate lower quality data going forward. |
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## Model Usage |
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The model is available for download on Hugging Face. It is suitable for a wide range of language tasks, from generating creative text to understanding and following complex instructions. |
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