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qwopus-glm-18b-merged
# 🪐 Qwen3.5-9B-GLM5.1-Distill-v1
## 📌 Model Overview
**Model Name:** `Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1`
**Base Model:** Qwen3.5-9B
**Training Type:** Supervised Fine-Tuning (SFT, Distillation)
**Parameter Scale:** 9B
**Training Framework:** Unsloth
This model is a distilled variant of **Qwen3.5-9B**, trained on high-quality reasoning data derived from **GLM-5.1**.
The primary goals are to:
- Improve **structured reasoning ability**
- Enhance **instruction-following consistency**
- Activate **latent knowledge via better reasoning structure**
## 📊 Training Data
### Main Dataset
- `Jackrong/GLM-5.1-Reasoning-1M-Cleaned`
- Cleaned from the original `Kassadin88/GLM-5.1-1000000x` dataset.
- Generated from a **GLM-5.1 teacher model**
- Approximately **700x** the scale of `Qwen3.5-reasoning-700x`
- Training used a **filtered subset**, not the full source dataset.
### Auxiliary Dataset
- `Jackrong/Qwen3.5-reasoning-700x`
...
Repository: localaiLicense: apache-2.0
qwen3.5-9b-glm5.1-distill-v1
# 🪐 Qwen3.5-9B-GLM5.1-Distill-v1
## 📌 Model Overview
**Model Name:** `Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1`
**Base Model:** Qwen3.5-9B
**Training Type:** Supervised Fine-Tuning (SFT, Distillation)
**Parameter Scale:** 9B
**Training Framework:** Unsloth
This model is a distilled variant of **Qwen3.5-9B**, trained on high-quality reasoning data derived from **GLM-5.1**.
The primary goals are to:
- Improve **structured reasoning ability**
- Enhance **instruction-following consistency**
- Activate **latent knowledge via better reasoning structure**
## 📊 Training Data
### Main Dataset
- `Jackrong/GLM-5.1-Reasoning-1M-Cleaned`
- Cleaned from the original `Kassadin88/GLM-5.1-1000000x` dataset.
- Generated from a **GLM-5.1 teacher model**
- Approximately **700x** the scale of `Qwen3.5-reasoning-700x`
- Training used a **filtered subset**, not the full source dataset.
### Auxiliary Dataset
- `Jackrong/Qwen3.5-reasoning-700x`
...
Repository: localaiLicense: apache-2.0
qwopus-glm-18b-merged
# 🪐 Qwen3.5-9B-GLM5.1-Distill-v1
## 📌 Model Overview
**Model Name:** `Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1`
**Base Model:** Qwen3.5-9B
**Training Type:** Supervised Fine-Tuning (SFT, Distillation)
**Parameter Scale:** 9B
**Training Framework:** Unsloth
This model is a distilled variant of **Qwen3.5-9B**, trained on high-quality reasoning data derived from **GLM-5.1**.
The primary goals are to:
- Improve **structured reasoning ability**
- Enhance **instruction-following consistency**
- Activate **latent knowledge via better reasoning structure**
## 📊 Training Data
### Main Dataset
- `Jackrong/GLM-5.1-Reasoning-1M-Cleaned`
- Cleaned from the original `Kassadin88/GLM-5.1-1000000x` dataset.
- Generated from a **GLM-5.1 teacher model**
- Approximately **700x** the scale of `Qwen3.5-reasoning-700x`
- Training used a **filtered subset**, not the full source dataset.
### Auxiliary Dataset
- `Jackrong/Qwen3.5-reasoning-700x`
...
Repository: localaiLicense: apache-2.0
huihui-glm-4.7-flash-abliterated-i1
The model is a quantized version of **huihui-ai/Huihui-GLM-4.7-Flash-abliterated**, optimized for efficiency and deployment. It uses GGUF files with various quantization levels (e.g., IQ1_M, IQ2_XXS, Q4_K_M) and is designed for tasks requiring low-resource deployment. Key features include:
- **Base Model**: Huihui-GLM-4.7-Flash-abliterated (unmodified, original model).
- **Quantization**: Supports IQ1_M to Q4_K_M, balancing accuracy and efficiency.
- **Use Cases**: Suitable for applications needing lightweight inference, such as edge devices or resource-constrained environments.
- **Downloads**: Available in GGUF format with varying quality and size (e.g., 0.2GB to 18.2GB).
- **Tags**: Abliterated, uncensored, and optimized for specific tasks.
This model is a modified version of the original GLM-4.7, tailored for deployment with quantized weights.
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
lfm2-vl-450m
LFM2‑VL is Liquid AI's first series of multimodal models, designed to process text and images with variable resolutions. Built on the LFM2 backbone, it is optimized for low-latency and edge AI applications.
We're releasing the weights of two post-trained checkpoints with 450M (for highly constrained devices) and 1.6B (more capable yet still lightweight) parameters.
2× faster inference speed on GPUs compared to existing VLMs while maintaining competitive accuracy
Flexible architecture with user-tunable speed-quality tradeoffs at inference time
Native resolution processing up to 512×512 with intelligent patch-based handling for larger images, avoiding upscaling and distortion
Repository: localaiLicense: lfm1.0
lfm2-vl-1.6b
LFM2‑VL is Liquid AI's first series of multimodal models, designed to process text and images with variable resolutions. Built on the LFM2 backbone, it is optimized for low-latency and edge AI applications.
We're releasing the weights of two post-trained checkpoints with 450M (for highly constrained devices) and 1.6B (more capable yet still lightweight) parameters.
2× faster inference speed on GPUs compared to existing VLMs while maintaining competitive accuracy
Flexible architecture with user-tunable speed-quality tradeoffs at inference time
Native resolution processing up to 512×512 with intelligent patch-based handling for larger images, avoiding upscaling and distortion
Repository: localaiLicense: lfm1.0
liquidai_lfm2-350m-extract
Based on LFM2-350M, LFM2-350M-Extract is designed to extract important information from a wide variety of unstructured documents (such as articles, transcripts, or reports) into structured outputs like JSON, XML, or YAML.
Use cases:
Extracting invoice details from emails into structured JSON.
Converting regulatory filings into XML for compliance systems.
Transforming customer support tickets into YAML for analytics pipelines.
Populating knowledge graphs with entities and attributes from unstructured reports.
You can find more information about other task-specific models in this blog post.
Repository: localai
liquidai_lfm2-1.2b-extract
Based on LFM2-1.2B, LFM2-1.2B-Extract is designed to extract important information from a wide variety of unstructured documents (such as articles, transcripts, or reports) into structured outputs like JSON, XML, or YAML.
Use cases:
Extracting invoice details from emails into structured JSON.
Converting regulatory filings into XML for compliance systems.
Transforming customer support tickets into YAML for analytics pipelines.
Populating knowledge graphs with entities and attributes from unstructured reports.
Repository: localai
liquidai_lfm2-1.2b-rag
Based on LFM2-1.2B, LFM2-1.2B-RAG is specialized in answering questions based on provided contextual documents, for use in RAG (Retrieval-Augmented Generation) systems.
Use cases:
Chatbot to ask questions about the documentation of a particular product.
Custom support with an internal knowledge base to provide grounded answers.
Academic research assistant with multi-turn conversations about research papers and course materials.
Repository: localai
liquidai_lfm2-1.2b-tool
Based on LFM2-1.2B, LFM2-1.2B-Tool is designed for concise and precise tool calling. The key challenge was designing a non-thinking model that outperforms similarly sized thinking models for tool use.
Use cases:
Mobile and edge devices requiring instant API calls, database queries, or system integrations without cloud dependency.
Real-time assistants in cars, IoT devices, or customer support, where response latency is critical.
Resource-constrained environments like embedded systems or battery-powered devices needing efficient tool execution.
Repository: localai
liquidai_lfm2-8b-a1b
LFM2 is a new generation of hybrid models developed by Liquid AI, specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency.
We're releasing the weights of our first MoE based on LFM2, with 8.3B total parameters and 1.5B active parameters.
LFM2-8B-A1B is the best on-device MoE in terms of both quality (comparable to 3-4B dense models) and speed (faster than Qwen3-1.7B).
Code and knowledge capabilities are significantly improved compared to LFM2-2.6B.
Quantized variants fit comfortably on high-end phones, tablets, and laptops.
Repository: localai
openai-gpt-oss-20b-abliterated-uncensored-neo-imatrix
These are NEO Imatrix GGUFs, NEO dataset by DavidAU.
NEO dataset improves overall performance, and is for all use cases.
This model uses Huihui-gpt-oss-20b-BF16-abliterated as a base which DE-CENSORS the model and removes refusals.
Example output below (creative; IQ4_NL), using settings below.
This model can be a little rough around the edges (due to abliteration) ; make sure you see the settings below for best operation.
It can also be creative, off the shelf crazy and rational too.
Enjoy!
Repository: localaiLicense: apache-2.0
arcee-ai_afm-4.5b
AFM-4.5B is a 4.5 billion parameter instruction-tuned model developed by Arcee.ai, designed for enterprise-grade performance across diverse deployment environments from cloud to edge. The base model was trained on a dataset of 8 trillion tokens, comprising 6.5 trillion tokens of general pretraining data followed by 1.5 trillion tokens of midtraining data with enhanced focus on mathematical reasoning and code generation. Following pretraining, the model underwent supervised fine-tuning on high-quality instruction datasets. The instruction-tuned model was further refined through reinforcement learning on verifiable rewards as well as for human preference. We use a modified version of TorchTitan for pretraining, Axolotl for supervised fine-tuning, and a modified version of Verifiers for reinforcement learning.
The development of AFM-4.5B prioritized data quality as a fundamental requirement for achieving robust model performance. We collaborated with DatologyAI, a company specializing in large-scale data curation. DatologyAI's curation pipeline integrates a suite of proprietary algorithms—model-based quality filtering, embedding-based curation, target distribution-matching, source mixing, and synthetic data. Their expertise enabled the creation of a curated dataset tailored to support strong real-world performance.
The model architecture follows a standard transformer decoder-only design based on Vaswani et al., incorporating several key modifications for enhanced performance and efficiency. Notable architectural features include grouped query attention for improved inference efficiency and ReLU^2 activation functions instead of SwiGLU to enable sparsification while maintaining or exceeding performance benchmarks.
The model available in this repo is the instruct model following supervised fine-tuning and reinforcement learning.
Repository: localaiLicense: aml
insightface-buffalo-s
Small insightface pack (SCRFD-500MF detector + MBF 512-d embedder +
genderage, ~159MB). Good fit for mid-range CPU deployments.
NON-COMMERCIAL RESEARCH USE ONLY.
Repository: localaiLicense: insightface-non-commercial
insightface-buffalo-sc
Ultra-small insightface pack (SCRFD-500MF + MBF recognition only, ~16MB).
NO landmarks, NO age/gender head — `/v1/face/analyze` returns empty
attributes for this pack. Ideal for edge/embedded deployments where
only verification and embedding are needed.
NON-COMMERCIAL RESEARCH USE ONLY.
Repository: localaiLicense: insightface-non-commercial
insightface-opencv-int8
Int8-quantized OpenCV Zoo face pair (YuNet int8 + SFace int8, ~12MB).
Roughly 3x smaller and noticeably faster on CPU than the fp32 variant
at comparable accuracy for face tasks. APACHE 2.0 — commercial-safe.
Weights are downloaded on install via LocalAI's gallery mechanism.
Repository: localaiLicense: apache-2.0
wespeaker-resnet34
Speaker recognition with WeSpeaker's ResNet34 trained on VoxCeleb,
exported to ONNX. 256-d embeddings, CPU-friendly — avoids the
PyTorch runtime entirely (onnxruntime only). APACHE 2.0.
Pair with the `speaker-recognition` backend's OnnxDirectEngine.
Use when ECAPA-TDNN's torch dependency is undesirable (small
images, edge deployments).
Repository: localaiLicense: apache-2.0
rfdetr-base
RF-DETR is a real-time, transformer-based object detection model architecture developed by Roboflow and released under the Apache 2.0 license.
RF-DETR is the first real-time model to exceed 60 AP on the Microsoft COCO benchmark alongside competitive performance at base sizes. It also achieves state-of-the-art performance on RF100-VL, an object detection benchmark that measures model domain adaptability to real world problems. RF-DETR is fastest and most accurate for its size when compared current real-time objection models.
RF-DETR is small enough to run on the edge using Inference, making it an ideal model for deployments that need both strong accuracy and real-time performance.
Repository: localaiLicense: apache-2.0
edgetam
EdgeTAM is an ultra-efficient variant of the Segment Anything Model (SAM) for image segmentation.
It uses a RepViT backbone and is only ~16MB quantized (Q4_0), making it ideal for edge deployment.
Supports point-prompted and box-prompted image segmentation via the /v1/detection endpoint.
Powered by sam3.cpp (C/C++ with GGML).
Repository: localaiLicense: apache-2.0
qwen3-235b-a22b-instruct-2507
We introduce the updated version of the Qwen3-235B-A22B non-thinking mode, named Qwen3-235B-A22B-Instruct-2507, featuring the following key enhancements:
Significant improvements in general capabilities, including instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage.
Substantial gains in long-tail knowledge coverage across multiple languages.
Markedly better alignment with user preferences in subjective and open-ended tasks, enabling more helpful responses and higher-quality text generation.
Enhanced capabilities in 256K long-context understanding.
Repository: localaiLicense: apache-2.0
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