{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/count-model-params","entry":"count_model_params","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":7,"n_papers_ran":5,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":6,"n_samples_ran":4,"n_samples_fingerprinted":0,"n_places":7,"n_places_pointer_only":4,"by_status":{"ran_honours":2,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":2,"unverified":2},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2503.01496","paper":"/paper/2503-01496","title":"Liger: Linearizing Large Language Models to Gated Recurrent Structures","date":"2025-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"opensparsellms/linearization","path":"training/utils.py","file_url":"https://github.com/opensparsellms/linearization/blob/HEAD/training/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e66e68f6be47ba56","mcp_get_code":{"code_sha256":"e66e68f6be47ba56"}},{"arxiv_id":"2407.02482","paper":"/paper/boosting-consistency-in-story-visualization","title":"Boosting Consistency in Story Visualization with Rich-Contextual Conditional Diffusion Models","date":"2024-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"muzishen/rcdms","path":"train_stage1.py","file_url":"https://github.com/muzishen/rcdms/blob/HEAD/train_stage1.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4b208faf97544161","mcp_get_code":{"code_sha256":"4b208faf97544161"}},{"arxiv_id":"2402.11722","paper":"/paper/invertible-fourier-neural-operators-for","title":"Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems","date":"2024-02-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bayesianaigroup/ifno","path":"utils.py","file_url":"https://github.com/bayesianaigroup/ifno/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fe699bab853b3193","mcp_get_code":{"code_sha256":"fe699bab853b3193"}},{"arxiv_id":"2310.06313","paper":"/paper/advancing-pose-guided-image-synthesis-with","title":"Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models","date":"2023-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tencent-ailab/PCDMs","path":"stage1_train_prior_model.py","file_url":"https://github.com/tencent-ailab/PCDMs/blob/HEAD/stage1_train_prior_model.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4b208faf97544161","mcp_get_code":{"code_sha256":"4b208faf97544161"}},{"arxiv_id":"2307.07872","paper":"/paper/does-double-descent-occur-in-self-supervised","title":"Does Double Descent Occur in Self-Supervised Learning?","date":"2023-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yonatangideoni/double_descent_tiny_paper","path":"experiment_utils.py","file_url":"https://github.com/yonatangideoni/double_descent_tiny_paper/blob/HEAD/experiment_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b2c8ca40a95153a9","mcp_get_code":{"code_sha256":"b2c8ca40a95153a9"}},{"arxiv_id":"2302.14794","paper":"/paper/meta-learning-to-bridge-vision-and-language","title":"Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot Learning","date":"2023-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ivonajdenkoska/multimodal-meta-learn","path":"src/coco_trainer.py","file_url":"https://github.com/ivonajdenkoska/multimodal-meta-learn/blob/HEAD/src/coco_trainer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fc0765d8ca001df7","mcp_get_code":{"code_sha256":"fc0765d8ca001df7"}},{"arxiv_id":"1802.03268","paper":"/paper/efficient-neural-architecture-search-via-1","title":"Efficient Neural Architecture Search via Parameter Sharing","date":"2018-02-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kaileymonn/Quantized-ENAS-ConvNets","path":"micro_child.py","file_url":"https://github.com/kaileymonn/Quantized-ENAS-ConvNets/blob/HEAD/micro_child.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4ad735e14ee6c0d4","mcp_get_code":{"code_sha256":"4ad735e14ee6c0d4"}}]}