{"schema_version":"onlylabs.public_signal.v1","title":"Arcee AI Model: arcee-ai/Arcee-Blitz","description":"Arcee AI model signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/5af8cd6f-d935-4816-9d1d-fbb1a85133dc","json_url":"https://onlylabs.fyi/signals/5af8cd6f-d935-4816-9d1d-fbb1a85133dc/signal.json","generated_at":"2026-06-11T20:26:38.070Z","evidence_latest_fetched_at":"2026-06-11T03:03:58.89376+00:00","signal_first_seen_at":"2026-06-06T01:50:05.756622+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/5af8cd6f-d935-4816-9d1d-fbb1a85133dc","signal_json":"https://onlylabs.fyi/signals/5af8cd6f-d935-4816-9d1d-fbb1a85133dc/signal.json","source":"https://huggingface.co/arcee-ai/Arcee-Blitz","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/Arcee-Blitz. 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We view it as a practical “workhorse” model that can tackle a range of tasks without the overhead of larger architectures. Quantizations GGUF quants are available [here](https://huggingface.co/arcee-ai/Arcee-Blitz-GGUF) AWQ quants are available [here](https://huggingface.co/arcee-ai/Arcee-Blitz-AWQ) Model Details - Architecture Base: Mistral-Small-24B-Instruct-2501 - Parameter Count: 24B - Distillation Data: - Merged Virtuoso pipeline with Mistral architecture, hotstarting the training with over 3B tokens of pretraining distillation from DeepSeek-V3 logits - Fine-Tuning and Post-Training: - After capturing core logits, we performed additional fine-tuning and distillation steps to enhance overall performance. - License: [Apache-2.0](https://huggingface.co/arcee-ai/Arcee-Maestro-7B-Preview#license) Improving World Knowledge Arcee-Blitz shows large improvements to performance on MMLU-Pro versus the original Mistral-Small-3, reflecting a dramatic increase in world knowledge. 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