{"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-compress-model","entry":"load_compress_model","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":15,"n_papers_ran":2,"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":7,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":15,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":6},"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":"2406.14670","paper":"/paper/exploring-design-choices-for-building","title":"Exploring Design Choices for Building Language-Specific LLMs","date":"2024-06-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"atutej/token-language-adaptation","path":"FastChat/fastchat/model/compression.py","file_url":"https://github.com/atutej/token-language-adaptation/blob/HEAD/FastChat/fastchat/model/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"648d40deadeb4493","mcp_get_code":{"code_sha256":"648d40deadeb4493"}},{"arxiv_id":"2406.04845","paper":"/paper/fedllm-bench-realistic-benchmarks-for","title":"FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models","date":"2024-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rui-ye/fedllm-bench","path":"compression.py","file_url":"https://github.com/rui-ye/fedllm-bench/blob/HEAD/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"5196732e9a84b6dc","mcp_get_code":{"code_sha256":"5196732e9a84b6dc"}},{"arxiv_id":"2403.07714","paper":"/paper/stabletoolbench-towards-stable-large-scale","title":"StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models","date":"2024-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"openbmb/toolbench","path":"toolbench/model/compression.py","file_url":"https://github.com/openbmb/toolbench/blob/HEAD/toolbench/model/compression.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":"5f9d4d17fb70af2b","mcp_get_code":{"code_sha256":"5f9d4d17fb70af2b"}},{"arxiv_id":"2403.04132","paper":"/paper/chatbot-arena-an-open-platform-for-evaluating","title":"Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference","date":"2024-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BirgerMoell/SwedishLLMBenchmark","path":"fastchat/model/compression.py","file_url":"https://github.com/BirgerMoell/SwedishLLMBenchmark/blob/HEAD/fastchat/model/compression.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":"5196732e9a84b6dc","mcp_get_code":{"code_sha256":"5196732e9a84b6dc"}},{"arxiv_id":"2403.02502","paper":"/paper/trial-and-error-exploration-based-trajectory","title":"Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents","date":"2024-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yifan-song793/eto","path":"fastchat/model/compression.py","file_url":"https://github.com/yifan-song793/eto/blob/HEAD/fastchat/model/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5196732e9a84b6dc","mcp_get_code":{"code_sha256":"5196732e9a84b6dc"}},{"arxiv_id":"2403.00813","paper":"/paper/urbangpt-spatio-temporal-large-language","title":"UrbanGPT: Spatio-Temporal Large Language Models","date":"2024-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hkuds/urbangpt","path":"urbangpt/model/compression.py","file_url":"https://github.com/hkuds/urbangpt/blob/HEAD/urbangpt/model/compression.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a730a935c1f257ae","mcp_get_code":{"code_sha256":"a730a935c1f257ae"}},{"arxiv_id":"2402.11746","paper":"/paper/language-models-are-homer-simpson-safety-re","title":"Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"declare-lab/red-instruct","path":"starling_training/fastchat/model/compression.py","file_url":"https://github.com/declare-lab/red-instruct/blob/HEAD/starling_training/fastchat/model/compression.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":"d1264ba024d70a79","mcp_get_code":{"code_sha256":"d1264ba024d70a79"}},{"arxiv_id":"2402.02130","paper":"/paper/rendering-graphs-for-graph-reasoning-in","title":"GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning","date":"2024-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"WEIYanbin1999/GITA","path":"fastchat/model/compression.py","file_url":"https://github.com/WEIYanbin1999/GITA/blob/HEAD/fastchat/model/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5196732e9a84b6dc","mcp_get_code":{"code_sha256":"5196732e9a84b6dc"}},{"arxiv_id":"2401.08326","paper":"/paper/rotbench-a-multi-level-benchmark-for","title":"RoTBench: A Multi-Level Benchmark for Evaluating the Robustness of Large Language Models in Tool Learning","date":"2024-01-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Junjie-Ye/RoTBench","path":"Code/model/compression.py","file_url":"https://github.com/Junjie-Ye/RoTBench/blob/HEAD/Code/model/compression.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":"5f9d4d17fb70af2b","mcp_get_code":{"code_sha256":"5f9d4d17fb70af2b"}},{"arxiv_id":"2312.00401","paper":"/paper/viotgpt-learning-to-schedule-vision-tools","title":"VIoTGPT: Learning to Schedule Vision Tools in LLMs towards Intelligent Video Internet of Things","date":"2023-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhongyy/viotgpt","path":"train/compression.py","file_url":"https://github.com/zhongyy/viotgpt/blob/HEAD/train/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5f9d4d17fb70af2b","mcp_get_code":{"code_sha256":"5f9d4d17fb70af2b"}},{"arxiv_id":"2310.17631","paper":"/paper/judgelm-fine-tuned-large-language-models-are","title":"JudgeLM: Fine-tuned Large Language Models are Scalable Judges","date":"2023-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hitz-zentroa/eval-MCG-COLING-2025","path":"evaluation/JudgeLM-main/judgelm/model/compression.py","file_url":"https://github.com/hitz-zentroa/eval-MCG-COLING-2025/blob/HEAD/evaluation/JudgeLM-main/judgelm/model/compression.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":"8cd841084972921e","mcp_get_code":{"code_sha256":"8cd841084972921e"}},{"arxiv_id":"2310.13023","paper":"/paper/graphgpt-graph-instruction-tuning-for-large","title":"GraphGPT: Graph Instruction Tuning for Large Language Models","date":"2023-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HKUDS/GraphGPT","path":"graphgpt/model/compression.py","file_url":"https://github.com/HKUDS/GraphGPT/blob/HEAD/graphgpt/model/compression.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a730a935c1f257ae","mcp_get_code":{"code_sha256":"a730a935c1f257ae"}},{"arxiv_id":"2112.05682","paper":"/paper/self-attention-does-not-need-o-n-2-memory","title":"Self-attention Does Not Need $O(n^2)$ Memory","date":"2021-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stability-ai/fastchat","path":"fastchat/model/compression.py","file_url":"https://github.com/stability-ai/fastchat/blob/HEAD/fastchat/model/compression.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":"8dacf011c6f243dc","mcp_get_code":{"code_sha256":"8dacf011c6f243dc"}},{"arxiv_id":"2025.emnlp-main.1337","paper":null,"title":"arXiv:2025.emnlp-main.1337","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"RazvanDu/CopySpec","path":"FastChat/fastchat/model/compression.py","file_url":"https://github.com/RazvanDu/CopySpec/blob/HEAD/FastChat/fastchat/model/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5196732e9a84b6dc","mcp_get_code":{"code_sha256":"5196732e9a84b6dc"}},{"arxiv_id":"2024.findings-emnlp.559","paper":null,"title":"arXiv:2024.findings-emnlp.559","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"hitz-zentroa/cn-eval","path":"evaluation/JudgeLM-main/judgelm/model/compression.py","file_url":"https://github.com/hitz-zentroa/cn-eval/blob/HEAD/evaluation/JudgeLM-main/judgelm/model/compression.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":"8cd841084972921e","mcp_get_code":{"code_sha256":"8cd841084972921e"}}]}