{"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/load-model-on-gpus","entry":"load_model_on_gpus","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":5,"n_papers_ran":0,"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":3,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":0,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":3},"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":"2507.11953","paper":null,"title":"arXiv:2507.11953","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"QwenLM/Qwen","path":"utils.py","file_url":"https://github.com/QwenLM/Qwen/blob/HEAD/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":"b10fc288b8419c43","mcp_get_code":{"code_sha256":"b10fc288b8419c43"}},{"arxiv_id":"2406.12793","paper":"/paper/chatglm-a-family-of-large-language-models","title":"ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools","date":"2024-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thudm/chatglm-6b","path":"utils.py","file_url":"https://github.com/thudm/chatglm-6b/blob/HEAD/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":"b461f4040f1eec9d","mcp_get_code":{"code_sha256":"b461f4040f1eec9d"}},{"arxiv_id":"2402.11905","paper":"/paper/learning-to-edit-aligning-llms-with-knowledge","title":"Learning to Edit: Aligning LLMs with Knowledge Editing","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yjiangcm/lte","path":"Qwen/utils.py","file_url":"https://github.com/yjiangcm/lte/blob/HEAD/Qwen/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":"b10fc288b8419c43","mcp_get_code":{"code_sha256":"b10fc288b8419c43"}},{"arxiv_id":"2306.04188","paper":"/paper/a-new-dataset-and-empirical-study-for","title":"A New Dataset and Empirical Study for Sentence Simplification in Chinese","date":"2023-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"THUDM/ChatGLM-6B","path":"utils.py","file_url":"https://github.com/THUDM/ChatGLM-6B/blob/HEAD/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":"b461f4040f1eec9d","mcp_get_code":{"code_sha256":"b461f4040f1eec9d"}},{"arxiv_id":"2303.17568","paper":"/paper/codegeex-a-pre-trained-model-for-code","title":"CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X","date":"2023-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thudm/codegeex2","path":"demo/gpus.py","file_url":"https://github.com/thudm/codegeex2/blob/HEAD/demo/gpus.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":"eb072eed78a57ca7","mcp_get_code":{"code_sha256":"eb072eed78a57ca7"}}]}