**Model type:** Video-LLaVA is an open-source multomodal model 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 **Model Description:** The model can generate interleaving images and videos, despite the absence of image-video pairs in the dataset. Video-LLaVa is uses an encoder trained for unified visual representation through alignment prior to projection. Extensive experiments demonstrate the complementarity of modalities, showcasing significant superiority when compared to models specifically designed for either images or videos. If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:.
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View details ↗> **Update (October 29, 2026): Q3_K_M and Q4_K_M republished with an anti-loop fine-tune merged in.** A small > LoRA trained with verifiable-reward RL (GRPO with distillation from the full model) on multi-language coding tasks > removes the reasoning loops of the quantized files. Measured on Q3_K_M with the fine-tune applied as an adapter (40 held-out coding tasks x 2, and the Aider > "hmm" test, without any logit bias). The republished files contain the same fine-tune merged into the weights: > > | Q3_K_M | Before | After | > |---|---|---| > | Held-out coding tasks: looping runs | 11 of 80 | **0 of 80** | > | Held-out coding tasks: solved | 46 of 80 | **51 of 80** | > | Aider "hmm" test: exercises with a loop | 4 of 14 | **0 of 14** (14 of 14 solved) | > > The `--logit-bias` "hmm" ban below is no longer needed for these files. Q8_0 is unchanged: it does not show the > loop. The previous Q3_K_M / Q4_K_M files remain available in the repository history. > **New release (October 2026).** This repository used to hold REAP144, a 144-expert version calibrated on Python > agentic sessions only. REAP144 looped on general questions and on other programming languages and could get simple > arithmetic wrong. It is replaced by **RCO-152E**: 152 of 288 experts per layer, chosen by optimization on a mix of > agentic sessions, code in 21 languages, math and general text, then healed. The old REAP144 files were removed; they > remain available in the repository history. Recipe, mask, patches and evaluation harness: github.com/lapadatbogdan/GLM-5.3-Flash-Coder-RCO-152E
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View details ↗We propose **CodeScaler**, an execution-free reward model designed to scale both reinforcement learning training and test-time inference for code generation. **CodeScaler** is trained on carefully curated preference data derived from verified code problems and incorporates syntax-aware code extraction and validity-preserving reward shaping to ensure stable and robust optimization. This model is the official CodeScaler-8B trained from Skywork/Skywork-Reward-V2-Qwen3-8B on LARK-Lab/CodeScalerPair-51K.
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View details ↗A LoRA for MiniMax-H3 that renders joint **video + synchronized stereo audio** in as few as **4 sampling steps** instead of the usual ~20 — a ~5× sampling speedup — and keeps getting better as you add steps. For **most** work, use **`minimax_h3_turbo_v4_step600_ema.safetensors`**. It's the strongest checkpoint we've released: much better static and small-motion shots, markedly better micro-detail (faces, fingers, fine texture), and the over-sharpening / plastic look of the earlier `v1` (~850) line is **fully resolved**. v4 introduced a **static-frame enhancement** — a big win for static and small-motion content. The one trade-off shows up **only at 4 steps with large, fast motion**, where v4 can produce **motion-smear / trailing ghosting** (we're actively fixing this). Two things address it:
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