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qwen3.6-35b-a3b-claude-4.6-opus-reasoning-distilled
# 🔥 Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled
A reasoning SFT fine-tune of `Qwen/Qwen3.6-35B-A3B` on chain-of-thought (CoT) distillation mostly sourced from Claude Opus 4.6. The goal is to preserve Qwen3.6's strong agentic coding and reasoning base while nudging the model toward structured Claude Opus-style reasoning traces and more stable long-form problem solving.
The training path is text-only. The Qwen3.6 base architecture includes a vision encoder, but this fine-tuning run did not train on image or video examples.
- **Developed by:** @hesamation
- **Base model:** `Qwen/Qwen3.6-35B-A3B`
- **License:** apache-2.0
This fine-tuning run is inspired by Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled, including the notebook/training workflow style and Claude Opus reasoning-distillation direction.
[](https://x.com/Hesamation) [](https://discord.gg/vtJykN3t)
## Benchmark Results
The MMLU-Pro pass used 70 total questions per model: `--limit 5` across 14 MMLU-Pro subjects. Treat this as a smoke/comparative check, not a release-quality full benchmark.
...
Repository: localaiLicense: apache-2.0
vllm-omni-wan2.2-t2v
Wan2.2-T2V-A14B via vLLM-Omni - Text-to-video generation model from Wan-AI. Generates high-quality videos from text prompts using a 14B parameter diffusion model.
Repository: localaiLicense: apache-2.0
vllm-omni-wan2.2-i2v
Wan2.2-I2V-A14B via vLLM-Omni - Image-to-video generation model from Wan-AI. Generates high-quality videos from images using a 14B parameter diffusion model.
Repository: localaiLicense: apache-2.0
vllm-omni-qwen3-omni-30b
Qwen3-Omni-30B-A3B-Instruct via vLLM-Omni - A large multimodal model (30B active, 3B activated per token) from Alibaba Qwen team. Supports text, image, audio, and video understanding with text and speech output. Features native multimodal understanding across all modalities.
Repository: localaiLicense: apache-2.0
qwen3-vl-reranker-8b
**Model Name:** Qwen3-VL-Reranker-8B
**Base Model:** Qwen/Qwen3-VL-Reranker-8B
**Description:**
A high-performance multimodal reranking model for state-of-the-art cross-modal search. It supports 30+ languages and handles text, images, screenshots, videos, and mixed modalities. With 8B parameters and a 32K context length, it refines retrieval results by combining embedding vectors with precise relevance scores. Optimized for efficiency, it supports quantized versions (e.g., Q8_0, Q4_K_M) and is ideal for applications requiring accurate multimodal content matching.
**Key Features:**
- **Multimodal**: Text, images, videos, and mixed content.
- **Language Support**: 30+ languages.
- **Quantization**: Available in Q8_0 (best quality), Q4_K_M (fast, recommended), and lower-precision options.
- **Performance**: Outperforms base models in retrieval tasks (e.g., JinaVDR, ViDoRe v3).
- **Use Case**: Enhances search pipelines by refining embeddings with precise relevance scores.
**Downloads:**
- [GGUF Files](https://huggingface.co/mradermacher/Qwen3-VL-Reranker-8B-GGUF) (e.g., `Qwen3-VL-Reranker-8B.Q8_0.gguf`).
**Usage:**
- Requires `transformers`, `qwen-vl-utils`, and `torch`.
- Example: `from scripts.qwen3_vl_reranker import Qwen3VLReranker; model = Qwen3VLReranker(...)`
**Citation:**
@article{qwen3vlembedding, ...}
This description emphasizes its capabilities, efficiency, and versatility for multimodal search tasks.
Repository: localai
qwen3-vl-reranker-2b-i1
**Model Name:** Qwen3-VL-Reranker-2B-i1
**Base Model:** Qwen/Qwen3-VL-Reranker-2B
**Description:**
A high-performance multimodal reranking model for state-of-the-art cross-modal search. It supports 30+ languages and handles text, images, screenshots, videos, and mixed modalities. With 8B parameters and a 32K context length, it refines retrieval results by combining embedding vectors with precise relevance scores. Optimized for efficiency, it supports quantized versions (e.g., Q8_0, Q4_K_M) and is ideal for applications requiring accurate multimodal content matching.
**Key Features:**
- **Multimodal**: Text, images, videos, and mixed content.
- **Language Support**: 30+ languages.
- **Quantization**: Available in Q8_0 (best quality), Q4_K_M (fast, recommended), and lower-precision options.
- **Performance**: Outperforms base models in retrieval tasks (e.g., JinaVDR, ViDoRe v3).
- **Use Case**: Enhances search pipelines by refining embeddings with precise relevance scores.
**Downloads:**
- [GGUF Files](https://huggingface.co/mradermacher/Qwen3-VL-Reranker-2B-i1-GGUF) (e.g., `Qwen3-VL-Reranker-2B.i1-Q4_K_M.gguf`).
**Usage:**
- Requires `transformers`, `qwen-vl-utils`, and `torch`.
- Example: `from scripts.qwen3_vl_reranker import Qwen3VLReranker; model = Qwen3VLReranker(...)`
**Citation:**
@article{qwen3vlembedding, ...}
This description emphasizes its capabilities, efficiency, and versatility for multimodal search tasks.
Repository: localai
qwen3-vl-30b-a3b-instruct
Meet Qwen3-VL — the most powerful vision-language model in the Qwen series to date.
This generation delivers comprehensive upgrades across the board: superior text understanding & generation, deeper visual perception & reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities.
Available in Dense and MoE architectures that scale from edge to cloud, with Instruct and reasoning‑enhanced Thinking editions for flexible, on-demand deployment.
#### Key Enhancements:
* **Visual Agent**: Operates PC/mobile GUIs—recognizes elements, understands functions, invokes tools, completes tasks.
* **Visual Coding Boost**: Generates Draw.io/HTML/CSS/JS from images/videos.
* **Advanced Spatial Perception**: Judges object positions, viewpoints, and occlusions; provides stronger 2D grounding and enables 3D grounding for spatial reasoning and embodied AI.
* **Long Context & Video Understanding**: Native 256K context, expandable to 1M; handles books and hours-long video with full recall and second-level indexing.
* **Enhanced Multimodal Reasoning**: Excels in STEM/Math—causal analysis and logical, evidence-based answers.
* **Upgraded Visual Recognition**: Broader, higher-quality pretraining is able to “recognize everything”—celebrities, anime, products, landmarks, flora/fauna, etc.
* **Expanded OCR**: Supports 32 languages (up from 19); robust in low light, blur, and tilt; better with rare/ancient characters and jargon; improved long-document structure parsing.
* **Text Understanding on par with pure LLMs**: Seamless text–vision fusion for lossless, unified comprehension.
#### Model Architecture Updates:
1. **Interleaved-MRoPE**: Full‑frequency allocation over time, width, and height via robust positional embeddings, enhancing long‑horizon video reasoning.
2. **DeepStack**: Fuses multi‑level ViT features to capture fine-grained details and sharpen image–text alignment.
3. **Text–Timestamp Alignment:** Moves beyond T‑RoPE to precise, timestamp‑grounded event localization for stronger video temporal modeling.
This is the weight repository for Qwen3-VL-30B-A3B-Instruct.
Repository: localaiLicense: apache-2.0
qwen3-omni-30b-a3b-instruct
Qwen3-Omni is the natively end-to-end multilingual omni-modal foundation model. It processes text, images, audio, and video, and delivers real-time streaming responses in both text and natural speech. This GGUF build runs on llama.cpp with the bundled mmproj for multimodal inputs.
Repository: localaiLicense: apache-2.0
ltx-2
**LTX-2** is a DiT-based audio-video foundation model designed to generate synchronized video and audio within a single model. It brings together the core building blocks of modern video generation, with open weights and a focus on practical, local execution.
**Key Features:**
- **Joint Audio-Video Generation**: Generates synchronized video and audio in a single model
- **Image-to-Video**: Converts static images into dynamic videos with matching audio
- **High Quality**: Produces realistic video with natural motion and synchronized audio
- **Open Weights**: Available under the LTX-2 Community License Agreement
**Model Details:**
- **Model Type**: Diffusion-based audio-video foundation model
- **Architecture**: DiT (Diffusion Transformer) based
- **Developed by**: Lightricks
- **Paper**: [LTX-2: Efficient Joint Audio-Visual Foundation Model](https://arxiv.org/abs/2601.03233)
**Usage Tips:**
- Width & height settings must be divisible by 32
- Frame count must be divisible by 8 + 1 (e.g., 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, 97, 105, 113, 121)
- Recommended settings: width=768, height=512, num_frames=121, frame_rate=24.0
- For best results, use detailed prompts describing motion and scene dynamics
**Limitations:**
- This model is not intended or able to provide factual information
- Prompt following is heavily influenced by the prompting-style
- When generating audio without speech, the audio may be of lower quality
**Citation:**
```bibtex
@article{hacohen2025ltx2,
title={LTX-2: Efficient Joint Audio-Visual Foundation Model},
author={HaCohen, Yoav and Brazowski, Benny and Chiprut, Nisan and others},
journal={arXiv preprint arXiv:2601.03233},
year={2025}
}
```
Repository: localaiLicense: ltx-2-community-license-agreement
smolvlm2-2.2b-instruct
SmolVLM2-2.2B is a lightweight multimodal model designed to analyze video content. The model processes videos, images, and text inputs to generate text outputs - whether answering questions about media files, comparing visual content, or transcribing text from images. Despite its compact size, requiring only 5.2GB of GPU RAM for video inference, it delivers robust performance on complex multimodal tasks. This efficiency makes it particularly well-suited for on-device applications where computational resources may be limited.
Repository: localaiLicense: apache-2.0
smolvlm2-500m-video-instruct
SmolVLM2-500M-Video is a lightweight multimodal model designed to analyze video content.
The model processes videos, images, and text inputs to generate text outputs - whether answering questions about media files, comparing visual content, or transcribing text from images. Despite its compact size, requiring only 1.8GB of GPU RAM for video inference, it delivers robust performance on complex multimodal tasks.
This efficiency makes it particularly well-suited for on-device applications where computational resources may be limited.
Repository: localaiLicense: apache-2.0
smolvlm2-256m-video-instruct
SmolVLM2-256M-Video is a lightweight multimodal model designed to analyze video content. The model processes videos, images, and text inputs to generate text outputs - whether answering questions about media files, comparing visual content, or transcribing text from images. Despite its compact size, requiring only 1.38GB of GPU RAM for video inference. This efficiency makes it particularly well-suited for on-device applications that require specific domain fine-tuning and computational resources may be limited.
Repository: localaiLicense: apache-2.0
gemma-3n-e2b-it
Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3n models are designed for efficient execution on low-resource devices. They are capable of multimodal input, handling text, image, video, and audio input, and generating text outputs, with open weights for pre-trained and instruction-tuned variants. These models were trained with data in over 140 spoken languages.
Gemma 3n models use selective parameter activation technology to reduce resource requirements. This technique allows the models to operate at an effective size of 2B and 4B parameters, which is lower than the total number of parameters they contain. For more information on Gemma 3n's efficient parameter management technology, see the Gemma 3n page.
Repository: localaiLicense: gemma
gemma-3n-e4b-it
Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3n models are designed for efficient execution on low-resource devices. They are capable of multimodal input, handling text, image, video, and audio input, and generating text outputs, with open weights for pre-trained and instruction-tuned variants. These models were trained with data in over 140 spoken languages.
Gemma 3n models use selective parameter activation technology to reduce resource requirements. This technique allows the models to operate at an effective size of 2B and 4B parameters, which is lower than the total number of parameters they contain. For more information on Gemma 3n's efficient parameter management technology, see the Gemma 3n page.
Repository: localaiLicense: gemma
wan-2.1-t2v-1.3b-ggml
Wan 2.1 T2V 1.3B — text-to-video diffusion model, GGUF-quantized for the
stable-diffusion.cpp backend. Generates short (33-frame) 832x480 clips
from a text prompt. Cheapest Wan variant, suitable for CPU-offloaded
inference with ~10 GB of usable RAM.
Repository: localaiLicense: apache-2.0
wan-2.1-i2v-14b-480p-ggml
Wan 2.1 I2V 14B 480P — image-to-video diffusion, GGUF Q4 quantization.
Animates a reference image into a 33-frame 480p clip. Requires more
RAM than the 1.3B T2V variant; CPU offload enabled by default.
Repository: localaiLicense: apache-2.0
wan-2.1-flf2v-14b-720p-ggml
Wan 2.1 FLF2V 14B 720P — first-last-frame-to-video diffusion, GGUF Q4_K_M.
Takes a start and end reference image and interpolates a 33-frame clip
between them. Unlike the plain I2V variant this model feeds the end
frame through clip_vision as well, so it conditions semantically (not
just in pixel-space) on both endpoints. That makes it the right choice
for seamless loops (start_image == end_image) and clean narrative cuts.
Native 720p but accepts 480p resolutions; shares the same VAE, t5xxl
text encoder, and clip_vision_h as I2V 14B.
Repository: localaiLicense: apache-2.0
wan-2.1-i2v-14b-720p-ggml
Wan 2.1 I2V 14B 720P — image-to-video diffusion, GGUF Q4_K_M.
Native 720p sibling of the 480p I2V model: animates a single
reference image into a 33-frame clip at up to 1280x720. Trained
purely as image-to-video (no first-last-frame interpolation path),
so motion is freer and better-suited to single-anchor animation
than repurposing the FLF2V 720P variant for i2v. Shares the same
VAE, umt5_xxl text encoder, and clip_vision_h as the I2V 14B 480P
and FLF2V 14B 720P entries.
Repository: localaiLicense: apache-2.0
ltx-2.3
**LTX-2.3** is an improved DiT-based audio-video foundation model from Lightricks, building upon the LTX-2 architecture with enhanced capabilities for generating synchronized video and audio within a single model.
**Key Features:**
- **Joint Audio-Video Generation**: Generates synchronized video and audio in a single model
- **Image-to-Video**: Converts static images into dynamic videos with matching audio
- **Enhanced Quality**: Improved video quality and motion generation over LTX-2
- **Open Weights**: Available under the LTX-2 Community License Agreement
**Model Details:**
- **Model Type**: Diffusion-based audio-video foundation model
- **Architecture**: DiT (Diffusion Transformer) based
- **Developed by**: Lightricks
- **Parent Model**: LTX-2
**Usage Tips:**
- Width & height settings must be divisible by 32
- Frame count must be divisible by 8 + 1 (e.g., 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, 97, 105, 113, 121)
- Recommended settings: width=768, height=512, num_frames=121, frame_rate=24.0
- For best results, use detailed prompts describing motion and scene dynamics
**Limitations:**
- This model is not intended or able to provide factual information
- Prompt following is heavily influenced by the prompting-style
- When generating audio without speech, the audio may be of lower quality
Repository: localaiLicense: ltx-2-community-license-agreement