{"schema_version":"onlylabs.public_signal.v1","title":"Arcee AI Model: arcee-ai/GLM-4-32B-Base-32K","description":"Arcee AI model signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/c525a4e1-e143-4008-83f5-8f63e0d71874","json_url":"https://onlylabs.fyi/signals/c525a4e1-e143-4008-83f5-8f63e0d71874/signal.json","generated_at":"2026-06-11T02:50:40.920594+00:00","org":{"slug":"arcee","name":"Arcee AI","category":"neolab","category_label":"Neolab","dossier_url":"https://onlylabs.fyi/labs/arcee","dossier_json_url":"https://onlylabs.fyi/labs/arcee/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/c525a4e1-e143-4008-83f5-8f63e0d71874","signal_json":"https://onlylabs.fyi/signals/c525a4e1-e143-4008-83f5-8f63e0d71874/signal.json","source":"https://huggingface.co/arcee-ai/GLM-4-32B-Base-32K","lab_dossier":"https://onlylabs.fyi/labs/arcee","lab_dossier_json":"https://onlylabs.fyi/labs/arcee/dossier.json","analysis":"https://onlylabs.fyi/analysis/arcee","analysis_json":"https://onlylabs.fyi/analysis/arcee/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/arcee/evidence.json","category":"https://onlylabs.fyi/neolabs","category_json":"https://onlylabs.fyi/neolabs.json","category_feed":"https://onlylabs.fyi/neolabs/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json?category=neolab","topic":"https://onlylabs.fyi/topics/releases","topic_signals_json":"https://onlylabs.fyi/topics/releases/signals.json?category=neolab","topic_feed":"https://onlylabs.fyi/topics/releases/feed.xml?category=neolab","data_business":null},"answer_pack":{"answer":"Arcee AI published arcee-ai/GLM-4-32B-Base-32K. 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While the original model's capabilities degraded after 8,192 tokens, this version maintains strong performance up to a 32,000-token context, making it ideal for tasks requiring long-context understanding and processing. This model was developed as a proof-of-concept to validate that a merging-centric approach to context extension can be successfully applied to larger-scale models. The techniques employed resulted in an approximate 5% overall improvement on standard base model benchmarks while significantly improving 32k recall. More details can be found in our blog post [here](https://www.arcee.ai/blog/extending-afm-4-5b-to-64k-context-length) where we applied this work to our upcoming AFM 4.5B Model Details - Architecture Base: [THUDM/GLM-4-32B-Base-0414](https://huggingface.co/THUDM/GLM-4-32B-Base-0414) - Parameter..."},"evidence_pages":[{"url":"https://huggingface.co/arcee-ai/GLM-4-32B-Base-32K/raw/main/README.md","final_url":"https://huggingface.co/arcee-ai/GLM-4-32B-Base-32K/raw/main/README.md","title":"arcee-ai/GLM-4-32B-Base-32K model card","http_status":200,"content_type":"text/plain; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-11T02:50:40.920594+00:00","bytes":3991,"raw_path":"928c6eb4451b54e17b5027019fd59fcb391cd0d1e2e542c54901209a65fe9ae4.md","content_hash":"4df3520370d0012a06435b56425b818b208fc9fb095bb9242ed247e055ff1d01","excerpt_chars":1200,"truncated":true,"excerpt":"--- base_model: - THUDM/GLM-4-32B-Base-0414 license: mit pipeline_tag: text-generation library_name: transformers language: - zh - en --- GLM-4-32B-Base-32K GLM-4-32B-Base-32K is an enhanced version of [THUDM's GLM-4-32B-Base-0414](https://huggingface.co/THUDM/GLM-4-32B-Base-0414), specifically engineered to offer robust performance over an extended context window. 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