162 lines
8.2 KiB
Markdown
162 lines
8.2 KiB
Markdown
---
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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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license:
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- mit
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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from diverse sources such as GPTeacher, the general, roleplay v1&2, code instruct datasets, Nous Instruct & PDACTL (unpublished), and several others, detailed further below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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Check out LM Studio for a nice chatgpt style interface here: https://lmstudio.ai/
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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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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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