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longcat-video
LongCat-Video served by LocalAI's dedicated CUDA backend. Generates video from a text prompt or a start image. The SDPA attention path works without FlashAttention and is suitable for CUDA 13 ARM64 systems such as DGX Spark. This is a very large checkpoint (roughly 83 GB in Hugging Face storage) and requires Linux with an NVIDIA CUDA GPU plus substantial memory and disk.

Repository: localaiLicense: mit

longcat-video-avatar-1.5
LongCat-Video-Avatar-1.5 served by LocalAI's dedicated CUDA backend. Turns speech plus a prompt into an avatar video, optionally conditioning on a portrait, and continues across multiple segments for longer audio. Avatar generation also loads tokenizer, text encoder, and VAE components from LongCat-Video. Plan for very large downloads and substantial NVIDIA GPU or unified memory; CPU and macOS execution are unsupported.

Repository: localaiLicense: mit

qwen3-4b-thinking-2507-gspo-easy
**Model Name:** Qwen3-4B-Thinking-2507-GSPO-Easy **Base Model:** Qwen3-4B (by Alibaba Cloud) **Fine-tuned With:** GRPO (Generalized Reward Policy Optimization) **Framework:** Hugging Face TRL (Transformers Reinforcement Learning) **License:** [MIT](https://huggingface.co/leonMW/Qwen3-4B-Thinking-2507-GSPO-Easy/blob/main/LICENSE) --- ### 📌 Description: A fine-tuned 4-billion-parameter version of **Qwen3-4B**, optimized for **step-by-step reasoning and complex problem-solving** using **GRPO**, a reinforcement learning method designed to enhance mathematical and logical reasoning in language models. This model excels in tasks requiring **structured thinking**, such as solving math problems, logical puzzles, and multi-step reasoning, making it ideal for applications in education, AI assistants, and reasoning benchmarks. ### 🔧 Key Features: - Trained with **TRL 0.23.1** and **Transformers 4.57.1** - Optimized for **high-quality reasoning output** - Part of the **Qwen3-4B-Thinking** series, designed to simulate human-like thought processes - Compatible with Hugging Face `transformers` and `pipeline` API ### 📚 Use Case: Perfect for applications demanding **deep reasoning**, such as: - AI tutoring systems - Advanced chatbots with explanation capabilities - Automated problem-solving in STEM domains ### 📌 Quick Start (Python): ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="leonMW/Qwen3-4B-Thinking-2507-GSPO-Easy", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` > ✅ **Note**: This is the **original, non-quantized base model**. Quantized versions (e.g., GGUF) are available separately under the same repository for efficient inference on consumer hardware. --- 🔗 **Model Page:** [https://huggingface.co/leonMW/Qwen3-4B-Thinking-2507-GSPO-Easy](https://huggingface.co/leonMW/Qwen3-4B-Thinking-2507-GSPO-Easy) 📝 **Training Details & Visualizations:** [WandB Dashboard](https://wandb.ai/leonwenderoth-tu-darmstadt/huggingface/runs/t42skrc7) --- *Fine-tuned using GRPO — a method proven to boost mathematical reasoning in open language models. Cite: Shao et al., 2024 (arXiv:2402.03300)*

Repository: localaiLicense: apache-2.0