Topic

Granite

2 posts about Granite. Every summary links to the original.

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Posts

Hugging Face
Granite Switch 4.1: Chat, Decisions, and Speech from One Endpoint
Granite Switch 4.1 models combine chat, general and specialized decisions, and speech-to-text from a single hosted endpoint. The 8B and 30B models include a new Decision aLoRA for general-purpose decision-making and a Guardian aLoRA that outperforms Jev 1.13 and other closed models on safety tasks. They also embed Granite-Speech-5.0-Turbo-CTC, the fastest ASR model on the OpenASR Leaderboard, enabling efficient transcription. All components are composed into one model using cache-efficient Activated LoRAs, taking up about the same GPU VRAM as the base model alone, and the KV cache from base generation can be reused for any subsequent adapter decisions. Granite Switch 4.1 can switch between chat, decision, and spoken requests quickly, and a 3B variant achieved 60-100 decisions per second playing Doom, compared to Jev’s 10, while also narrating and answering questions.

Red Hat
Build an AI matching engine with Docling, Granite & OpenShift AI
PeopleMesh uses Docling and Granite4:3B LLM on Red Hat OpenShift to extract structured data from PDF resumes and match people across multiple dimensions. Docling converts documents into a DoclingDocument with semantic labels using an AI model trained on 150,000 annotated samples to recover reading order from PDF’s print-oriented encoding, then exports to Markdown for LLM-driven JSON extraction via a system prompt. For matching, PeopleMesh decomposes a user query with an LLM to extract intent, entity type, country, and language, then applies structured filtering followed by semantic vector search via cosine similarity on distilled query embeddings. To mitigate embedding dilution from concatenated concepts, PeopleMesh uses a weighted scoring algorithm that reranks top 100 candidates using 7 information channels including nice-to-have skills and penalty multipliers for missing must-have and negative skills. The scoring provides relative ordering only, not normalized probabilities, and default weights are adjustable via the SearchOptions Java record.

The best engineering reads, once a week.

Topic

2 posts about Granite. Every summary links to the original.

Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.

Newest first

Hugging Face
Granite Switch 4.1: Chat, Decisions, and Speech from One Endpoint
Granite Switch 4.1 models combine chat, general and specialized decisions, and speech-to-text from a single hosted endpoint. The 8B and 30B models include a new Decision aLoRA for general-purpose decision-making and a Guardian aLoRA that outperforms Jev 1.13 and other closed models on safety tasks. They also embed Granite-Speech-5.0-Turbo-CTC, the fastest ASR model on the OpenASR Leaderboard, enabling efficient transcription. All components are composed into one model using cache-efficient Activated LoRAs, taking up about the same GPU VRAM as the base model alone, and the KV cache from base generation can be reused for any subsequent adapter decisions. Granite Switch 4.1 can switch between chat, decision, and spoken requests quickly, and a 3B variant achieved 60-100 decisions per second playing Doom, compared to Jev’s 10, while also narrating and answering questions.

Red Hat
Build an AI matching engine with Docling, Granite & OpenShift AI
PeopleMesh uses Docling and Granite4:3B LLM on Red Hat OpenShift to extract structured data from PDF resumes and match people across multiple dimensions. Docling converts documents into a DoclingDocument with semantic labels using an AI model trained on 150,000 annotated samples to recover reading order from PDF’s print-oriented encoding, then exports to Markdown for LLM-driven JSON extraction via a system prompt. For matching, PeopleMesh decomposes a user query with an LLM to extract intent, entity type, country, and language, then applies structured filtering followed by semantic vector search via cosine similarity on distilled query embeddings. To mitigate embedding dilution from concatenated concepts, PeopleMesh uses a weighted scoring algorithm that reranks top 100 candidates using 7 information channels including nice-to-have skills and penalty multipliers for missing must-have and negative skills. The scoring provides relative ordering only, not normalized probabilities, and default weights are adjustable via the SearchOptions Java record.

The best engineering reads, once a week.