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ced-base-f16
CED (Consistent Ensemble Distillation, Xiaomi) is a sound-event classifier that tags everyday sounds (baby cry, footsteps, glass breaking, alarms, dog bark, ...) into the 527-class AudioSet ontology. This is the f16 GGUF for the ced backend (a standalone C++/ggml port). Recommended default: fastest on CPU and near-lossless. Use POST /v1/audio/classification, or the realtime websocket API for live recognition.

Repository: localaiLicense: apache-2.0

ced-tiny-f16
CED-tiny (5.5M params, Pi-class / edge) sound-event classifier over the 527-class AudioSet ontology (baby cry, footsteps, glass breaking, alarms, dog bark, ...). f16 GGUF for the ced backend (recommended (fastest on CPU)). Use POST /v1/audio/classification, or the realtime websocket API for live recognition.

Repository: localaiLicense: apache-2.0

ced-mini-f16
CED-mini (9.6M params, low-power) sound-event classifier over the 527-class AudioSet ontology (baby cry, footsteps, glass breaking, alarms, dog bark, ...). f16 GGUF for the ced backend (recommended (fastest on CPU)). Use POST /v1/audio/classification, or the realtime websocket API for live recognition.

Repository: localaiLicense: apache-2.0

ced-small-f16
CED-small (22M params, balanced size/accuracy) sound-event classifier over the 527-class AudioSet ontology (baby cry, footsteps, glass breaking, alarms, dog bark, ...). f16 GGUF for the ced backend (recommended (fastest on CPU)). Use POST /v1/audio/classification, or the realtime websocket API for live recognition.

Repository: localaiLicense: apache-2.0

allenai_olmo-3.1-32b-think
The **Olmo-3.1-32B-Think** model is a large language model (LLM) optimized for efficient inference using quantized versions. It is a quantized version of the original **allenai/Olmo-3.1-32B-Think** model, developed by **bartowski** using the **imatrix** quantization method. ### Key Features: - **Base Model**: `allenai/Olmo-3.1-32B-Think` (unquantized version). - **Quantized Versions**: Available in multiple formats (e.g., `Q6_K_L`, `Q4_1`, `bf16`) with varying precision (e.g., Q8_0, Q6_K_L, Q5_K_M). These are derived from the original model using the **imatrix calibration dataset**. - **Performance**: Optimized for low-memory usage and efficient inference on GPUs/CPUs. Recommended quantization types include `Q6_K_L` (near-perfect quality) or `Q4_K_M` (default, balanced performance). - **Downloads**: Available via Hugging Face CLI. Split into multiple files if needed for large models. - **License**: Apache-2.0. ### Recommended Quantization: - Use `Q6_K_L` for highest quality (near-perfect performance). - Use `Q4_K_M` for balanced performance and size. - Avoid lower-quality options (e.g., `Q3_K_S`) unless specific hardware constraints apply. This model is ideal for deploying on GPUs/CPUs with limited memory, leveraging efficient quantization for practical use cases.

Repository: localaiLicense: apache-2.0

huihui-ai_huihui-gpt-oss-20b-bf16-abliterated
This is an uncensored version of unsloth/gpt-oss-20b-BF16 created with abliteration (see remove-refusals-with-transformers to know more about it).

Repository: localaiLicense: apache-2.0

depth-anything-3-base-f16
Depth Anything 3 (base), f16 — half precision (~233 MB), no measurable accuracy loss vs f32. Depth + camera pose.

Repository: localaiLicense: apache-2.0

depth-anything-2-base-f16
Depth Anything V2 (base / ViT-B), f16 — half precision, no measurable accuracy loss vs f32. Relative monocular depth only (no pose). Use GenerateImage (src -> depth PNG) or the Depth endpoint.

Repository: localaiLicense: apache-2.0

qwen3-reranker-0.6b
The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining. **Exceptional Versatility**: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks No.1 in the MTEB multilingual leaderboard (as of June 5, 2025, score 70.58), while the reranking model excels in various text retrieval scenarios. **Comprehensive Flexibility**: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios. **Multilingual Capability**: The Qwen3 Embedding series offer support for over 100 languages, thanks to the multilingual capabilites of Qwen3 models. This includes various programming languages, and provides robust multilingual, cross-lingual, and code retrieval capabilities. **Qwen3-Reranker-0.6B** has the following features: - Model Type: Text Reranking - Supported Languages: 100+ Languages - Number of Paramaters: 0.6B - Context Length: 32k - Quantization: q4_K_M, q5_0, q5_K_M, q6_K, q8_0, f16

Repository: localaiLicense: apache-2.0

qwen3-embedding-4b
The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining. **Exceptional Versatility**: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks **No.1** in the MTEB multilingual leaderboard (as of June 5, 2025, score **70.58**), while the reranking model excels in various text retrieval scenarios. **Comprehensive Flexibility**: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios. **Multilingual Capability**: The Qwen3 Embedding series offer support for over 100 languages, thanks to the multilingual capabilites of Qwen3 models. This includes various programming languages, and provides robust multilingual, cross-lingual, and code retrieval capabilities. **Qwen3-Embedding-4B-GGUF** has the following features: - Model Type: Text Embedding - Supported Languages: 100+ Languages - Number of Paramaters: 4B - Context Length: 32k - Embedding Dimension: Up to 2560, supports user-defined output dimensions ranging from 32 to 2560 - Quantization: q4_K_M, q5_0, q5_K_M, q6_K, q8_0, f16

Repository: localaiLicense: apache-2.0

qwen3-embedding-8b
The Qwen3 Embedding series model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining. **Exceptional Versatility**: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks **No.1** in the MTEB multilingual leaderboard (as of June 5, 2025, score **70.58**), while the reranking model excels in various text retrieval scenarios. **Comprehensive Flexibility**: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios. **Multilingual Capability**: The Qwen3 Embedding series offer support for over 100 languages, thanks to the multilingual capabilites of Qwen3 models. This includes various programming languages, and provides robust multilingual, cross-lingual, and code retrieval capabilities. **Qwen3-Embedding-8B-GGUF** has the following features: - Model Type: Text Embedding - Supported Languages: 100+ Languages - Number of Paramaters: 8B - Context Length: 32k - Embedding Dimension: Up to 4096, supports user-defined output dimensions ranging from 32 to 4096 - Quantization: q4_K_M, q5_0, q5_K_M, q6_K, q8_0, f16

Repository: localaiLicense: apache-2.0

qwen3-embedding-0.6b
The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining. **Exceptional Versatility**: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks **No.1** in the MTEB multilingual leaderboard (as of June 5, 2025, score **70.58**), while the reranking model excels in various text retrieval scenarios. **Comprehensive Flexibility**: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios. **Multilingual Capability**: The Qwen3 Embedding series offer support for over 100 languages, thanks to the multilingual capabilites of Qwen3 models. This includes various programming languages, and provides robust multilingual, cross-lingual, and code retrieval capabilities. **Qwen3-Embedding-0.6B-GGUF** has the following features: - Model Type: Text Embedding - Supported Languages: 100+ Languages - Number of Paramaters: 0.6B - Context Length: 32k - Embedding Dimension: Up to 1024, supports user-defined output dimensions ranging from 32 to 1024 - Quantization: q8_0, f16

Repository: localaiLicense: apache-2.0

gpt-oss-20b-esper3.1-i1
**Model Name:** gpt-oss-20b-Esper3.1 **Repository:** [ValiantLabs/gpt-oss-20b-Esper3.1](https://huggingface.co/ValiantLabs/gpt-oss-20b-Esper3.1) **Base Model:** openai/gpt-oss-20b **Type:** Instruction-tuned, reasoning-focused language model **Size:** 20 billion parameters **License:** Apache 2.0 --- ### 🔍 **Overview** gpt-oss-20b-Esper3.1 is a specialized, instruction-tuned variant of the 20B open-source GPT model, developed by **Valiant Labs**. It excels in **advanced coding, software architecture, and DevOps reasoning**, making it ideal for technical problem-solving and AI-driven engineering tasks. ### ✨ **Key Features** - **Expert in DevOps & Cloud Systems:** Trained on high-difficulty datasets (e.g., Titanium3, Tachibana3, Mitakihara), it delivers precise, actionable guidance for AWS, Kubernetes, Terraform, Ansible, Docker, Jenkins, and more. - **Strong Code Reasoning:** Optimized for complex programming tasks, including full-stack development, scripting, and debugging. - **High-Quality Inference:** Uses `bf16` precision for full-precision performance; quantized versions (e.g., GGUF) available for efficient local inference. - **Open-Source & Free to Use:** Fully open-access, built on the public gpt-oss-20b foundation and trained with community datasets. ### 📌 **Use Cases** - Designing scalable cloud architectures - Writing and optimizing infrastructure-as-code - Debugging complex DevOps pipelines - AI-assisted software development and documentation - Real-time technical troubleshooting ### 💡 **Getting Started** Use the standard `text-generation` pipeline with the `transformers` library. Supports role-based prompting (e.g., `user`, `assistant`) and performs best with high-reasoning prompts. ```python from transformers import pipeline pipe = pipeline("text-generation", model="ValiantLabs/gpt-oss-20b-Esper3.1", torch_dtype="auto", device_map="auto") messages = [{"role": "user", "content": "Design a Kubernetes cluster for a high-traffic web app with CI/CD via GitHub Actions."}] outputs = pipe(messages, max_new_tokens=2000) print(outputs[0]["generated_text"][-1]) ``` --- > 🔗 **Model Gallery Entry**: > *gpt-oss-20b-Esper3.1 – A powerful, open-source 20B model tuned for expert-level DevOps, coding, and system architecture. Built by Valiant Labs using high-quality technical datasets. Perfect for engineers, architects, and AI developers.*

Repository: localaiLicense: apache-2.0

spiral-qwen3-4b-multi-env
**Model Name:** Spiral-Qwen3-4B-Multi-Env **Base Model:** Qwen3-4B (fine-tuned variant) **Repository:** [spiral-rl/Spiral-Qwen3-4B-Multi-Env](https://huggingface.co/spiral-rl/Spiral-Qwen3-4B-Multi-Env) **Quantized Version:** Available via GGUF (by mradermacher) --- ### 📌 Description: Spiral-Qwen3-4B-Multi-Env is a fine-tuned, instruction-optimized version of the Qwen3-4B language model, specifically enhanced for multi-environment reasoning and complex task execution. Built upon the foundational Qwen3-4B architecture, this model demonstrates strong performance in coding, logical reasoning, and domain-specific problem-solving across diverse environments. The model was developed by **spiral-rl**, with contributions from the community, and is designed to support advanced, real-world applications requiring robust reasoning, adaptability, and structured output generation. It is optimized for use in constrained environments, making it ideal for edge deployment and low-latency inference. --- ### 🔧 Key Features: - **Architecture:** Qwen3-4B (Decoder-only, Transformer-based) - **Fine-tuned For:** Multi-environment reasoning, instruction following, and complex task automation - **Language Support:** English (primary), with strong multilingual capability - **Model Size:** 4 billion parameters - **Training Data:** Proprietary and public datasets focused on reasoning, coding, and task planning - **Use Case:** Ideal for agent-based systems, automated workflows, and intelligent decision-making in dynamic environments --- ### 📦 Availability: While the original base model is hosted at `spiral-rl/Spiral-Qwen3-4B-Multi-Env`, a **quantized GGUF version** is available for efficient inference on consumer hardware: - **Repository:** [mradermacher/Spiral-Qwen3-4B-Multi-Env-GGUF](https://huggingface.co/mradermacher/Spiral-Qwen3-4B-Multi-Env-GGUF) - **Quantizations:** Q2_K to Q8_0 (including IQ4_XS), f16, and Q4_K_M recommended for balance of speed and quality --- ### 💡 Ideal For: - Local AI agents - Edge deployment - Code generation and debugging - Multi-step task planning - Research in low-resource reasoning systems --- > ✅ **Note:** The model card above reflects the *original, unquantized base model*. The quantized version (GGUF) is optimized for performance but may have minor quality trade-offs. For full fidelity, use the base model with full precision.

Repository: localaiLicense: apache-2.0