cjwbw / lorahub
Efficient Cross-Task Generalization via Dynamic LoRA Composition
Prediction
cjwbw/lorahub:02a0166b0b1deeec3e794ced2dc6c670592302e158c9ec51b676279afd0e6238Input
- example_inputs
- Infer the date from context. Q: Today, 8/3/1997, is a day that we will never forget. What is the date one week ago from today in MM/DD/YYYY? Options: (A) 03/27/1998 (B) 09/02/1997 (C) 07/27/1997 (D) 06/29/1997 (E) 07/27/1973 (F) 12/27/1997 A: Infer the date from context. Q: May 6, 1992 is like yesterday to Jane, but that is actually ten years ago. What is the date tomorrow in MM/DD/YYYY? Options: (A) 04/16/2002 (B) 04/07/2003 (C) 05/07/2036 (D) 05/28/2002 (E) 05/07/2002 A: Infer the date from context. Q: Today is the second day of the third month of 1966. What is the date one week ago from today in MM/DD/YYYY? Options: (A) 02/26/1966 (B) 01/13/1966 (C) 02/02/1966 (D) 10/23/1966 (E) 02/23/1968 (F) 02/23/1966 A:
- example_outputs
- (C) (E) (F)
- max_inference_step
- 40
- num_random_lora_modules
- 20
{ "example_inputs": "Infer the date from context. Q: Today, 8/3/1997, is a day that we will never forget. What is the date one week ago from today in MM/DD/YYYY? Options: (A) 03/27/1998 (B) 09/02/1997 (C) 07/27/1997 (D) 06/29/1997 (E) 07/27/1973 (F) 12/27/1997 A:\nInfer the date from context. Q: May 6, 1992 is like yesterday to Jane, but that is actually ten years ago. What is the date tomorrow in MM/DD/YYYY? Options: (A) 04/16/2002 (B) 04/07/2003 (C) 05/07/2036 (D) 05/28/2002 (E) 05/07/2002 A:\nInfer the date from context. Q: Today is the second day of the third month of 1966. What is the date one week ago from today in MM/DD/YYYY? Options: (A) 02/26/1966 (B) 01/13/1966 (C) 02/02/1966 (D) 10/23/1966 (E) 02/23/1968 (F) 02/23/1966 A:", "example_outputs": "(C)\n(E)\n(F)", "max_inference_step": 40, "num_random_lora_modules": 20 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cjwbw/lorahub using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cjwbw/lorahub:02a0166b0b1deeec3e794ced2dc6c670592302e158c9ec51b676279afd0e6238", { input: { example_inputs: "Infer the date from context. Q: Today, 8/3/1997, is a day that we will never forget. What is the date one week ago from today in MM/DD/YYYY? Options: (A) 03/27/1998 (B) 09/02/1997 (C) 07/27/1997 (D) 06/29/1997 (E) 07/27/1973 (F) 12/27/1997 A:\nInfer the date from context. Q: May 6, 1992 is like yesterday to Jane, but that is actually ten years ago. What is the date tomorrow in MM/DD/YYYY? Options: (A) 04/16/2002 (B) 04/07/2003 (C) 05/07/2036 (D) 05/28/2002 (E) 05/07/2002 A:\nInfer the date from context. Q: Today is the second day of the third month of 1966. What is the date one week ago from today in MM/DD/YYYY? Options: (A) 02/26/1966 (B) 01/13/1966 (C) 02/02/1966 (D) 10/23/1966 (E) 02/23/1968 (F) 02/23/1966 A:", example_outputs: "(C)\n(E)\n(F)", max_inference_step: 40, num_random_lora_modules: 20 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun cjwbw/lorahub using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cjwbw/lorahub:02a0166b0b1deeec3e794ced2dc6c670592302e158c9ec51b676279afd0e6238", input={ "example_inputs": "Infer the date from context. Q: Today, 8/3/1997, is a day that we will never forget. What is the date one week ago from today in MM/DD/YYYY? Options: (A) 03/27/1998 (B) 09/02/1997 (C) 07/27/1997 (D) 06/29/1997 (E) 07/27/1973 (F) 12/27/1997 A:\nInfer the date from context. Q: May 6, 1992 is like yesterday to Jane, but that is actually ten years ago. What is the date tomorrow in MM/DD/YYYY? Options: (A) 04/16/2002 (B) 04/07/2003 (C) 05/07/2036 (D) 05/28/2002 (E) 05/07/2002 A:\nInfer the date from context. Q: Today is the second day of the third month of 1966. What is the date one week ago from today in MM/DD/YYYY? Options: (A) 02/26/1966 (B) 01/13/1966 (C) 02/02/1966 (D) 10/23/1966 (E) 02/23/1968 (F) 02/23/1966 A:", "example_outputs": "(C)\n(E)\n(F)", "max_inference_step": 40, "num_random_lora_modules": 20 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cjwbw/lorahub using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "cjwbw/lorahub:02a0166b0b1deeec3e794ced2dc6c670592302e158c9ec51b676279afd0e6238", "input": { "example_inputs": "Infer the date from context. Q: Today, 8/3/1997, is a day that we will never forget. What is the date one week ago from today in MM/DD/YYYY? Options: (A) 03/27/1998 (B) 09/02/1997 (C) 07/27/1997 (D) 06/29/1997 (E) 07/27/1973 (F) 12/27/1997 A:\\nInfer the date from context. Q: May 6, 1992 is like yesterday to Jane, but that is actually ten years ago. What is the date tomorrow in MM/DD/YYYY? Options: (A) 04/16/2002 (B) 04/07/2003 (C) 05/07/2036 (D) 05/28/2002 (E) 05/07/2002 A:\\nInfer the date from context. Q: Today is the second day of the third month of 1966. What is the date one week ago from today in MM/DD/YYYY? Options: (A) 02/26/1966 (B) 01/13/1966 (C) 02/02/1966 (D) 10/23/1966 (E) 02/23/1968 (F) 02/23/1966 A:", "example_outputs": "(C)\\n(E)\\n(F)", "max_inference_step": 40, "num_random_lora_modules": 20 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-03T14:12:06.138705Z", "created_at": "2023-08-03T14:09:35.629561Z", "data_removed": false, "error": null, "id": "ujqqyibbie76zmqgtx4fbsx7gm", "input": { "example_inputs": "Infer the date from context. Q: Today, 8/3/1997, is a day that we will never forget. What is the date one week ago from today in MM/DD/YYYY? Options: (A) 03/27/1998 (B) 09/02/1997 (C) 07/27/1997 (D) 06/29/1997 (E) 07/27/1973 (F) 12/27/1997 A:\nInfer the date from context. Q: May 6, 1992 is like yesterday to Jane, but that is actually ten years ago. What is the date tomorrow in MM/DD/YYYY? Options: (A) 04/16/2002 (B) 04/07/2003 (C) 05/07/2036 (D) 05/28/2002 (E) 05/07/2002 A:\nInfer the date from context. Q: Today is the second day of the third month of 1966. What is the date one week ago from today in MM/DD/YYYY? Options: (A) 02/26/1966 (B) 01/13/1966 (C) 02/02/1966 (D) 10/23/1966 (E) 02/23/1968 (F) 02/23/1966 A:", "example_outputs": "(C)\n(E)\n(F)", "max_inference_step": 40, "num_random_lora_modules": 20 }, "logs": "Using seed: 15460\nDownloading (…)/adapter_config.json: 0%| | 0.00/411 [00:00<?, ?B/s]\nDownloading (…)/adapter_config.json: 100%|██████████| 411/411 [00:00<00:00, 1.08MB/s]\nDownloading (…)lve/main/config.json: 0%| | 0.00/662 [00:00<?, ?B/s]\nDownloading (…)lve/main/config.json: 100%|██████████| 662/662 [00:00<00:00, 2.15MB/s]\nDownloading model.safetensors: 0%| | 0.00/3.13G [00:00<?, ?B/s]\nDownloading model.safetensors: 0%| | 10.5M/3.13G [00:00<00:47, 65.2MB/s]\nDownloading model.safetensors: 1%| | 21.0M/3.13G [00:00<00:44, 70.0MB/s]\nDownloading model.safetensors: 1%| | 31.5M/3.13G [00:00<00:39, 78.7MB/s]\nDownloading model.safetensors: 1%|▏ | 41.9M/3.13G [00:00<00:36, 85.2MB/s]\nDownloading model.safetensors: 2%|▏ | 52.4M/3.13G [00:00<00:36, 85.3MB/s]\nDownloading 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jobs\nLaunching 1 jobs with new suggestions\nUpdating fitness with value 0.13169783075650532\n28 remaining budget and 0 running jobs\nLaunching 1 jobs with new suggestions\nUpdating fitness with value 0.11195437947909037\n27 remaining budget and 0 running jobs\nLaunching 1 jobs with new suggestions\nUpdating fitness with value 0.14315053780873618\n26 remaining budget and 0 running jobs\nLaunching 1 jobs with new suggestions\nUpdating fitness with value 0.09870247999827067\n25 remaining budget and 0 running jobs\nLaunching 1 jobs with new suggestions\nUpdating fitness with value 0.09308040221532185\n24 remaining budget and 0 running jobs\nLaunching 1 jobs with new suggestions\nUpdating fitness with value 0.08350652158260345\n23 remaining budget and 0 running jobs\nLaunching 1 jobs with new suggestions\nUpdating fitness with value 0.1701445213953654\n22 remaining budget and 0 running jobs\nLaunching 1 jobs with new suggestions\nUpdating fitness with value 0.08785513858000438\n21 remaining budget and 0 running jobs\nLaunching 1 jobs with new suggestions\nUpdating fitness with value 0.18224913199742637\n20 remaining budget and 0 running jobs\nLaunching 1 jobs with new suggestions\nUpdating fitness with value 0.887497984568278\n19 remaining budget and 0 running jobs\nLaunching 1 jobs with new suggestions\nUpdating fitness with value 0.20113089762185843\n18 remaining budget and 0 running jobs\nLaunching 1 jobs with new suggestions\nUpdating fitness with value 0.08463106314341227\n17 remaining budget and 0 running jobs\nLaunching 1 jobs with new suggestions\nUpdating fitness with value 0.179056189929146\n16 remaining budget and 0 running jobs\nLaunching 1 jobs with new suggestions\nUpdating fitness with value 0.07683334509531657\n15 remaining budget and 0 running jobs\nLaunching 1 jobs with new suggestions\nUpdating fitness with value 0.17642755043471342\n14 remaining budget and 0 running jobs\nLaunching 1 jobs with new suggestions\nUpdating fitness with value 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fitness with value 0.05956370060788695\n5 remaining budget and 0 running jobs\nLaunching 1 jobs with new suggestions\nUpdating fitness with value 0.13098620907509662\n4 remaining budget and 0 running jobs\nLaunching 1 jobs with new suggestions\nUpdating fitness with value 0.06969356769947482\n3 remaining budget and 0 running jobs\nLaunching 1 jobs with new suggestions\nUpdating fitness with value 0.09138107303580391\n2 remaining budget and 0 running jobs\nLaunching 1 jobs with new suggestions\nUpdating fitness with value 0.061673278693635536\n1 remaining budget and 0 running jobs\nLaunching 1 jobs with new suggestions\nUpdating fitness with value 0.045564029637715844\n0 remaining budget and 0 running jobs\nThe recommended weight set for the LoRA modules is:\n0.4984: lorahub/flan_t5_large-wmt16_translate_ro-en\n-0.2598: lorahub/flan_t5_large-anli_r2\n0.4774: lorahub/flan_t5_large-gem_e2e_nlg\n1.0626: lorahub/flan_t5_large-quartz_read_passage_below_choose\n0.0428: lorahub/flan_t5_large-wiki_qa_Decide_good_answer\n0.4685: lorahub/flan_t5_large-race_high_Select_the_best_answer_no_instructions_\n-0.0285: lorahub/flan_t5_large-app_reviews_convert_to_rating\n-0.0458: lorahub/flan_t5_large-glue_mnli\n-0.0571: lorahub/flan_t5_large-sentiment140\n0.5707: lorahub/flan_t5_large-quail_context_description_question_answer_id\n0.5296: lorahub/flan_t5_large-snli\n0.3255: lorahub/flan_t5_large-dbpedia_14_pick_one_category_for_the_following_text\n-0.0600: lorahub/flan_t5_large-quac\n0.5097: lorahub/flan_t5_large-quoref_What_Is_The_Answer\n0.5041: lorahub/flan_t5_large-wiqa_what_might_be_the_last_step_of_the_process\n0.5070: lorahub/flan_t5_large-super_glue_cb\n-0.1103: lorahub/flan_t5_large-quarel_heres_a_story\n-0.0032: lorahub/flan_t5_large-race_middle_Is_this_the_right_answer\n-0.0723: lorahub/flan_t5_large-adversarial_qa_dbidaf_answer_the_following_q\n0.0988: lorahub/flan_t5_large-race_middle_Select_the_best_answer_generate_span_", "metrics": { "predict_time": 146.179959, "total_time": 150.509144 }, "output": "https://replicate.delivery/pbxt/ICinfYhrpdRyeEMt78Za2IkPAh0751Lf1uLvBjX5pWFl8ssiA/out.bin", "started_at": "2023-08-03T14:09:39.958746Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ujqqyibbie76zmqgtx4fbsx7gm", "cancel": "https://api.replicate.com/v1/predictions/ujqqyibbie76zmqgtx4fbsx7gm/cancel" }, "version": "02a0166b0b1deeec3e794ced2dc6c670592302e158c9ec51b676279afd0e6238" }Generated inUsing seed: 15460 Downloading (…)/adapter_config.json: 0%| | 0.00/411 [00:00<?, ?B/s] Downloading (…)/adapter_config.json: 100%|██████████| 411/411 [00:00<00:00, 1.08MB/s] Downloading (…)lve/main/config.json: 0%| | 0.00/662 [00:00<?, ?B/s] Downloading (…)lve/main/config.json: 100%|██████████| 662/662 [00:00<00:00, 2.15MB/s] Downloading model.safetensors: 0%| | 0.00/3.13G [00:00<?, ?B/s] Downloading model.safetensors: 0%| | 10.5M/3.13G [00:00<00:47, 65.2MB/s] Downloading model.safetensors: 1%| | 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Downloading adapter_model.bin: 100%|██████████| 19.0M/19.0M [00:00<00:00, 58.6MB/s] > Loading lorahub/flan_t5_large-wiki_qa_Decide_good_answer ... 20%|██ | 4/20 [00:04<00:17, 1.09s/it] Downloading (…)/adapter_config.json: 0%| | 0.00/411 [00:00<?, ?B/s] Downloading (…)/adapter_config.json: 100%|██████████| 411/411 [00:00<00:00, 1.59MB/s] Downloading adapter_model.bin: 0%| | 0.00/19.0M [00:00<?, ?B/s] Downloading adapter_model.bin: 100%|██████████| 19.0M/19.0M [00:00<00:00, 190MB/s] > Loading lorahub/flan_t5_large-race_high_Select_the_best_answer_no_instructions_ ... 25%|██▌ | 5/20 [00:04<00:15, 1.03s/it] Downloading (…)/adapter_config.json: 0%| | 0.00/411 [00:00<?, ?B/s] Downloading (…)/adapter_config.json: 100%|██████████| 411/411 [00:00<00:00, 2.03MB/s] Downloading adapter_model.bin: 0%| | 0.00/19.0M [00:00<?, ?B/s] Downloading adapter_model.bin: 100%|██████████| 19.0M/19.0M [00:00<00:00, 121MB/s] Downloading adapter_model.bin: 100%|██████████| 19.0M/19.0M [00:00<00:00, 114MB/s] > 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> Loading lorahub/flan_t5_large-race_middle_Select_the_best_answer_generate_span_ ... 95%|█████████▌| 19/20 [00:22<00:01, 1.23s/it] Downloading (…)/adapter_config.json: 0%| | 0.00/411 [00:00<?, ?B/s] Downloading (…)/adapter_config.json: 100%|██████████| 411/411 [00:00<00:00, 1.31MB/s] Downloading adapter_model.bin: 0%| | 0.00/19.0M [00:00<?, ?B/s] Downloading adapter_model.bin: 55%|█████▌ | 10.5M/19.0M [00:00<00:00, 93.8MB/s] Downloading adapter_model.bin: 100%|██████████| 19.0M/19.0M [00:00<00:00, 107MB/s] 100%|██████████| 20/20 [00:23<00:00, 1.12s/it] 100%|██████████| 20/20 [00:23<00:00, 1.17s/it] Running tokenizer on dataset: 0%| | 0/3 [00:00<?, ? examples/s] Running tokenizer on dataset: 100%|██████████| 3/3 [00:00<00:00, 112.81 examples/s] > Begin to perform gradient-free optimization ... 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Launching 1 jobs with new suggestions Updating fitness with value 0.18811706304550171 31 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.18308404684066773 30 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.15466183026631675 29 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.13169783075650532 28 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.11195437947909037 27 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.14315053780873618 26 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.09870247999827067 25 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.09308040221532185 24 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.08350652158260345 23 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.1701445213953654 22 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.08785513858000438 21 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.18224913199742637 20 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.887497984568278 19 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.20113089762185843 18 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.08463106314341227 17 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.179056189929146 16 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.07683334509531657 15 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.17642755043471342 14 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.06571920676354828 13 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.0742276668098602 12 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.05183583021748306 11 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.12994661993059228 10 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.08364694323973731 9 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.08870862409630972 8 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.08821432639038829 7 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.05691506158264563 6 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.05956370060788695 5 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.13098620907509662 4 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.06969356769947482 3 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.09138107303580391 2 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.061673278693635536 1 remaining budget and 0 running jobs Launching 1 jobs with new suggestions Updating fitness with value 0.045564029637715844 0 remaining budget and 0 running jobs The recommended weight set for the LoRA modules is: 0.4984: lorahub/flan_t5_large-wmt16_translate_ro-en -0.2598: lorahub/flan_t5_large-anli_r2 0.4774: lorahub/flan_t5_large-gem_e2e_nlg 1.0626: lorahub/flan_t5_large-quartz_read_passage_below_choose 0.0428: lorahub/flan_t5_large-wiki_qa_Decide_good_answer 0.4685: lorahub/flan_t5_large-race_high_Select_the_best_answer_no_instructions_ -0.0285: lorahub/flan_t5_large-app_reviews_convert_to_rating -0.0458: lorahub/flan_t5_large-glue_mnli -0.0571: lorahub/flan_t5_large-sentiment140 0.5707: lorahub/flan_t5_large-quail_context_description_question_answer_id 0.5296: lorahub/flan_t5_large-snli 0.3255: lorahub/flan_t5_large-dbpedia_14_pick_one_category_for_the_following_text -0.0600: lorahub/flan_t5_large-quac 0.5097: lorahub/flan_t5_large-quoref_What_Is_The_Answer 0.5041: lorahub/flan_t5_large-wiqa_what_might_be_the_last_step_of_the_process 0.5070: lorahub/flan_t5_large-super_glue_cb -0.1103: lorahub/flan_t5_large-quarel_heres_a_story -0.0032: lorahub/flan_t5_large-race_middle_Is_this_the_right_answer -0.0723: lorahub/flan_t5_large-adversarial_qa_dbidaf_answer_the_following_q 0.0988: lorahub/flan_t5_large-race_middle_Select_the_best_answer_generate_span_
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