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--- |
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license: cc-by-nc-sa-4.0 |
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language: |
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- en |
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library_name: transformers |
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tags: |
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- UNA |
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- juanako |
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- mixtral |
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- MoE |
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--- |
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# UNAversal - Uniform Neural Alignment (MoE) |
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This is just a beta, a first release so people can start working on franksteins and so. |
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It does achieve high GSM/Math and TQA, so ideally you can merge it with other mixtrals and see what coming out of it |
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Based on [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co./mistralai/Mixtral-8x7B-Instruct-v0.1) |
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## UNA Details |
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For this model we came out with the most obvious, placing UNA on the router_logit. It does work, but we saw a much better performance on SFT by doing so. |
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So this model DOES have UNA-SFT phase, its highly experimental and it was merely using LLaMA-Factory datasets by example alpaca. |
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As the others: |
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- Can be finetuned further, try 2e-5 or **1e-4 (since its MOE)** |
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- Can be merged, here you will have to improvise and please report findings on a discussion thread. |
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**REMINDER**: please.. cite, it does help on the research and the lab itself, seriously. |
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## NEED YOUR HELP!! |
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I need a multi-turn trainloop for the Mixtral, that can squeeze the juice out of 8xH100's properly. Please feel free to reach @fblgit either discord or twitter. thanks! |
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# Evals |
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Here there are some, but we also submitted it to the HF eval queue.... |
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## GSM8k 5-Shot |
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``` |
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|Tasks|Version| Filter |n-shot| Metric |Value | |Stderr| |
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|-----|-------|----------|-----:|-----------|-----:|---|-----:| |
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|gsm8k|Yaml |get-answer| 5|exact_match|0.6603|± | 0.013| |
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``` |
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## ARC 25-Shot |
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``` |
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| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr| |
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|-------------|-------|------|-----:|--------|-----:|---|-----:| |
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|arc_challenge|Yaml |none | 25|acc |0.6621|± |0.0138| |
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| | |none | 25|acc_norm|0.6962|± |0.0134| |
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``` |
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## TruthfulQA 0-Shot (MC2) |
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``` |
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| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr| |
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|--------------|-------|------|-----:|------|-----:|---|-----:| |
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|truthfulqa_mc2|Yaml |none | 0|acc |0.7122|± |0.0141| |
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``` |
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## 0-Shots Evals |
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``` |
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| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr| |
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|--------------|-------|------|-----:|----------|-----:|---|-----:| |
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|arc_challenge |Yaml |none | 0|acc |0.6101|± |0.0143| |
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| | |none | 0|acc_norm |0.6425|± |0.0140| |
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|arc_easy |Yaml |none | 0|acc |0.8615|± |0.0071| |
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| | |none | 0|acc_norm |0.8375|± |0.0076| |
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|boolq |Yaml |none | 0|acc |0.8624|± |0.0060| |
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|lambada_openai|Yaml |none | 0|perplexity|2.8318|± |0.0507| |
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| | |none | 0|acc |0.7650|± |0.0059| |
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|mathqa |Yaml |none | 0|acc |0.4472|± |0.0091| |
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| | |none | 0|acc_norm |0.4436|± |0.0091| |
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|piqa |Yaml |none | 0|acc |0.8292|± |0.0088| |
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| | |none | 0|acc_norm |0.8422|± |0.0085| |
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|pubmedqa |Yaml |none | 0|acc |0.7920|± |0.0182| |
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|sciq |Yaml |none | 0|acc |0.9630|± |0.0060| |
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| | |none | 0|acc_norm |0.9370|± |0.0077| |
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``` |
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## BBH at 0-Shot |
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``` |
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vllm (pretrained=fblgit/UNAversal-8x7B-v1beta,tensor_parallel_size=2,data_parallel_size=4,gpu_memory_utilization=0.8,dtype=float16), gen_kwargs: (None), limit: None, num_fewshot: 0, batch_size: auto |
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| Tasks |Version| Filter |n-shot| Metric |Value | |Stderr| |
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|----------------------------------------------------------|-------|----------|-----:|-----------|-----:|---|-----:| |
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|bbh |N/A |get-answer| 0|exact_match|0.6752|± |0.1772| |
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| - bbh_cot_fewshot_boolean_expressions |Yaml |get-answer| 0|exact_match|0.8840|± |0.0203| |
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| - bbh_cot_fewshot_causal_judgement |Yaml |get-answer| 0|exact_match|0.6417|± |0.0352| |
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| - bbh_cot_fewshot_date_understanding |Yaml |get-answer| 0|exact_match|0.7600|± |0.0271| |
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| - bbh_cot_fewshot_disambiguation_qa |Yaml |get-answer| 0|exact_match|0.7160|± |0.0286| |
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| - bbh_cot_fewshot_dyck_languages |Yaml |get-answer| 0|exact_match|0.1800|± |0.0243| |
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| - bbh_cot_fewshot_formal_fallacies |Yaml |get-answer| 0|exact_match|0.6520|± |0.0302| |
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| - bbh_cot_fewshot_geometric_shapes |Yaml |get-answer| 0|exact_match|0.3880|± |0.0309| |
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| - bbh_cot_fewshot_hyperbaton |Yaml |get-answer| 0|exact_match|0.9600|± |0.0124| |
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| - bbh_cot_fewshot_logical_deduction_five_objects |Yaml |get-answer| 0|exact_match|0.5360|± |0.0316| |
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| - bbh_cot_fewshot_logical_deduction_seven_objects |Yaml |get-answer| 0|exact_match|0.5040|± |0.0317| |
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| - bbh_cot_fewshot_logical_deduction_three_objects |Yaml |get-answer| 0|exact_match|0.8600|± |0.0220| |
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| - bbh_cot_fewshot_movie_recommendation |Yaml |get-answer| 0|exact_match|0.7840|± |0.0261| |
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| - bbh_cot_fewshot_multistep_arithmetic_two |Yaml |get-answer| 0|exact_match|0.6600|± |0.0300| |
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| - bbh_cot_fewshot_navigate |Yaml |get-answer| 0|exact_match|0.8160|± |0.0246| |
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| - bbh_cot_fewshot_object_counting |Yaml |get-answer| 0|exact_match|0.8360|± |0.0235| |
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| - bbh_cot_fewshot_penguins_in_a_table |Yaml |get-answer| 0|exact_match|0.7329|± |0.0367| |
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| - bbh_cot_fewshot_reasoning_about_colored_objects |Yaml |get-answer| 0|exact_match|0.8120|± |0.0248| |
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| - bbh_cot_fewshot_ruin_names |Yaml |get-answer| 0|exact_match|0.4440|± |0.0315| |
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| - bbh_cot_fewshot_salient_translation_error_detection |Yaml |get-answer| 0|exact_match|0.5200|± |0.0317| |
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| - bbh_cot_fewshot_snarks |Yaml |get-answer| 0|exact_match|0.7135|± |0.0340| |
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| - bbh_cot_fewshot_sports_understanding |Yaml |get-answer| 0|exact_match|0.9400|± |0.0151| |
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| - bbh_cot_fewshot_temporal_sequences |Yaml |get-answer| 0|exact_match|0.7560|± |0.0272| |
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| - bbh_cot_fewshot_tracking_shuffled_objects_five_objects |Yaml |get-answer| 0|exact_match|0.5680|± |0.0314| |
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| - bbh_cot_fewshot_tracking_shuffled_objects_seven_objects|Yaml |get-answer| 0|exact_match|0.6280|± |0.0306| |
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| - bbh_cot_fewshot_tracking_shuffled_objects_three_objects|Yaml |get-answer| 0|exact_match|0.6280|± |0.0306| |
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| - bbh_cot_fewshot_web_of_lies |Yaml |get-answer| 0|exact_match|0.9560|± |0.0130| |
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| - bbh_cot_fewshot_word_sorting |Yaml |get-answer| 0|exact_match|0.3800|± |0.0308| |
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|Groups|Version| Filter |n-shot| Metric |Value | |Stderr| |
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|------|-------|----------|-----:|-----------|-----:|---|-----:| |
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|bbh |N/A |get-answer| 0|exact_match|0.6752|± |0.1772| |
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``` |
