The following quantizations have been renamed: - `Q6_K_S` → `Q6_K` - `Q5_K_S` → `Q5_K_XXS` - `Q4_K_S` → `Q4_K_XXS` - `Q3_K_S` → `Q3_K_XXS` - `Q2_K_S` → removed (mostly noisy and unusable) Subjective listening notes from current tests: - `Q8_0`: quality remains strong with little audible loss in typical samples. - `Q6_K`: still sounds good and is often a practical choice. - `Q5_K_M` / `Q5_K_S` (new): maintain decent quality, good results. - `Q5_K_XXS` (formerly `Q5_K_S`): slightly higher than but close to `Q4_K_M`. - `Q5` family (`Q5_1` / `Q5_K_M` / `Q5_K_S` / `Q5_0` / `Q5_K_XXS`): generally usable with moderate degradation. - `Q4_K_M` / `Q4_K_S` (new): quality drops compared to Q5, occasional muffled audio, but mostly acceptable. - `Q4` family (including `Q4_1` / `Q4_K_XXS` / `Q4_0`): audible pronunciation artifacts, often muffled. - `Q3_K_M` / `Q3_K_S` (new): further quality degradation, audio is muffled, slightly below but close to `Q4_K_XXS`. - `Q3_K_XXS` (formerly `Q3_K_S`): aggressive quantization, limited quality. - `Q2_K_L` (new): significant quality drop, quieter and muffled audio with noise. - `Q2_K` (new): severe artifacts and noise, but speech content can still be discerned. - ~~`Q2_K_S`~~ (removed): mostly degrades into noise and is usually unusable.
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Plant leaf disease classifier for the capstone project **AI-Powered Plant Disease Detection and Farmer Assistance** (IIIT-H, Group 4). A farmer uploads a photo of a crop leaf; this model returns the most likely PlantVillage class and a confidence score. A LangGraph agent then explains the result and suggests treatment from a curated agricultural knowledge base. Confusion matrix: `reports/confusion_matrix.png` · Full per-class report: `reports/classification_report.txt` Coverage is the share of test images with top-1 probability at or above the threshold; the remaining images would be answered with "not sure". Recommended `CONFIDENCE_THRESHOLD` for the API: **0.5** (lowest threshold whose accuracy on covered images is at least 97% with coverage of at least 50%).
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> [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc. Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency. GPT-OSS-120B GPT-OSS-20B Qwen3-Next-80B-A3B-Thinking Qwen3-30BA3B-Thinking-2507 Qwen3.5-9B Qwen3.5-4B
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**What's New in V2:** - 🎯 **Trained on higher quality data** for significantly improved results - 💪 **Better command responsiveness** - the model follows your prompts more accurately - 🖼️ **Fixed black bar artifacts** - no more unwanted black borders on generated images - ⚡ **Overall enhanced performance** - smoother transitions and better cinematic flow **next-scene-qwen-image-lora-2509** is a **LoRA adapter fine-tuned on Qwen-Image-Edit (build 2509)**, purpose-built to generate cinematic image sequences with natural visual progression from frame to frame. This model enables Qwen Image Edit to think like a film director—understanding camera dynamics, visual composition, and narrative continuity to create shots that flow seamlessly into one another.
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`FLUX.1 [dev]` is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. For more information, please read our blog post. 1. Cutting-edge output quality, second only to our state-of-the-art model `FLUX.1 [pro]`. 2. Competitive prompt following, matching the performance of closed source alternatives . 3. Trained using guidance distillation, making `FLUX.1 [dev]` more efficient. 4. Open weights to drive new scientific research, and empower artists to develop innovative workflows. 5. Generated outputs can be used for personal, scientific, and commercial purposes as described in the [`FLUX.1 [dev]` Non-Commercial License](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md). We provide a reference implementation of `FLUX.1 [dev]`, as well as sampling code, in a dedicated github repository. Developers and creatives looking to build on top of `FLUX.1 [dev]` are encouraged to use this as a starting point.
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This model has been pushed to the Hub using the PytorchModelHubMixin integration: - Library: https://github.com/lpiccinelli-eth/UniDepth - Docs: [More Information Needed]
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This model has been pushed to the Hub using the PytorchModelHubMixin integration: - Library: https://github.com/lpiccinelli-eth/UniDepth - Docs: [More Information Needed]
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