**Model type:** LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Base LLM: mistralai/Mistral-7B-Instruct-v0.2 **Where to send questions or comments about the model:** https://github.com/haotian-liu/LLaVA/issues **Primary intended uses:** The primary use of LLaVA is research on large multimodal models and chatbots.
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View details ↗**Model type:** LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Base LLM: lmsys/vicuna-13b-v1.5 Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. **Where to send questions or comments about the model:** https://github.com/haotian-liu/LLaVA/issues
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View details ↗**Model type:** LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Base LLM: lmsys/vicuna-7b-v1.5 Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. **Where to send questions or comments about the model:** https://github.com/haotian-liu/LLaVA/issues
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View details ↗An open-weights language model for contextually-aware end-of-utterance (EOU) detection in voice AI applications. The model predicts whether a user has finished speaking based on the semantic content of their transcribed speech, providing a critical complement to voice activity detection (VAD) systems. > **📖 For installation, usage examples, and integration guides, see the LiveKit documentation.** - Overview - Model Variants - How It Works - Architecture and Training - Supported Languages - Benchmarks - Usage - Deployment Requirements - Limitations - License - Resources
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View details ↗llama.cpp port of perplexity-ai/pplx-decider-v1.1-27b, a decision model: it does not generate text, it answers typed questions about a shared state and returns calibrated probabilities. Same request shape as TypeSafe's Jev (`state` + `noul` / `choice` / `score` questions). The upstream checkpoint is bf16 safetensors that needs torch and ~49 GB of weights. This repo makes it runnable under llama.cpp. Both quants carry the decision head inside the GGUF as `cls.output.weight`; `readout.safetensors` is shipped as well so a runtime can load it independently.
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View details ↗llama.cpp builds of **Rune 26B-A4B v3**, the decision model by Invergent. Each file is measured against the original bf16 weights. Livesport serves **IQ3_S-dyn** in production on **2× Quadro RTX 4000 8 GB** behind a Jev/SystemOne-compatible `/v1/systemone` API. Rune does not generate text. Given a *state* and a question with a set of options, it returns a probability for every option, read from the logits of the option letters at the first generated position. The upstream card describes the model, its training protocol and its Decision Index results. This card covers only what is specific to these quantizations. **Text only:** the files contain the language model, without Gemma 4's vision tower, so image states are not supported. The first version of this repository (one IQ3_M in three shards, KL 0.0450) is at revision `4990396`. IQ3_S-dyn replaces it: 1.2 GB larger, with half the KL against bf16.
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