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ornith-1.5-9b-obliterated-q4
Ornith-1.5-9B OBLITERATED is a refusal-removed derivative for alignment research, red teaming, coding, reasoning, and agentic tasks. Its safety guardrails are removed, and the publisher reports some capability loss compared with the original model. This default entry uses the Q4_K_M GGUF and BF16 vision projector. The linked variant uses the higher-quality Q8_0 quantization.

Repository: localaiLicense: mit

ornith-1.5-9b-obliterated-q8
Ornith-1.5-9B OBLITERATED in the higher-quality Q8_0 GGUF format, with the shared BF16 vision projector. Its safety guardrails are removed, and the publisher recommends this quantization for better behavior fidelity.

Repository: localaiLicense: mit

qwen3.8-27b-obliterated-q4
Qwen3.8-27B OBLITERATED is an Apache-2.0 Qwen3.8 vision-language model modified for refusal-removal and red-team research. It retains reasoning, coding, tool use, image, and video capabilities, but its safety guardrails have been removed. This default entry uses the Q4_K_M GGUF and BF16 vision projector. The linked variant uses the higher-quality Q8_0 model. The publisher recommends greedy decoding with a 1.15 repetition penalty.

Repository: localaiLicense: apache-2.0

qwen3.8-27b-obliterated-q8
Qwen3.8-27B OBLITERATED in the higher-quality Q8_0 GGUF format. This model is modified for refusal-removal and red-team research, and its safety guardrails have been removed.

Repository: localaiLicense: apache-2.0

qwen3-8b-jailbroken
This jailbroken LLM is released strictly for academic research purposes in AI safety and model alignment studies. The author bears no responsibility for any misuse or harm resulting from the deployment of this model. Users must comply with all applicable laws and ethical guidelines when conducting research. A jailbroken Qwen3-8B model using weight orthogonalization[1]. Implementation script: https://gist.github.com/cooperleong00/14d9304ba0a4b8dba91b60a873752d25 [1]: Arditi, Andy, et al. "Refusal in language models is mediated by a single direction." arXiv preprint arXiv:2406.11717 (2024).

Repository: localaiLicense: apache-2.0

granite-3.0-1b-a400m-instruct
Granite 3.0 language models are a new set of lightweight state-of-the-art, open foundation models that natively support multilinguality, coding, reasoning, and tool usage, including the potential to be run on constrained compute resources. All the models are publicly released under an Apache 2.0 license for both research and commercial use. The models' data curation and training procedure were designed for enterprise usage and customization in mind, with a process that evaluates datasets for governance, risk and compliance (GRC) criteria, in addition to IBM's standard data clearance process and document quality checks. Granite 3.0 includes 4 different models of varying sizes: Dense Models: 2B and 8B parameter models, trained on 12 trillion tokens in total. Mixture-of-Expert (MoE) Models: Sparse 1B and 3B MoE models, with 400M and 800M activated parameters respectively, trained on 10 trillion tokens in total. Accordingly, these options provide a range of models with different compute requirements to choose from, with appropriate trade-offs with their performance on downstream tasks. At each scale, we release a base model — checkpoints of models after pretraining, as well as instruct checkpoints — models finetuned for dialogue, instruction-following, helpfulness, and safety.

Repository: localaiLicense: apache-2.0

llama-3.2-1b-instruct:q4_k_m
The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Repository: localaiLicense: llama3.2

llama-3.2-3b-instruct:q4_k_m
The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Repository: localaiLicense: llama3.2

llama-3.2-3b-instruct:q8_0
The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Repository: localaiLicense: llama3.2

llama-3.2-1b-instruct:q8_0
The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Repository: localaiLicense: llama3.2

meta-llama-3.1-8b-instruct
The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks. Model developer: Meta Model Architecture: Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Repository: localaiLicense: llama3.1

meta-llama-3.1-70b-instruct
The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks. Model developer: Meta Model Architecture: Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Repository: localaiLicense: llama3.1

meta-llama-3.1-8b-claude-imat
Meta-Llama-3.1-8B-Claude-iMat-GGUF: Quantized from Meta-Llama-3.1-8B-Claude fp16. Weighted quantizations were creating using fp16 GGUF and groups_merged.txt in 88 chunks and n_ctx=512. Static fp16 will also be included in repo. For a brief rundown of iMatrix quant performance, please see this PR. All quants are verified working prior to uploading to repo for your safety and convenience.

Repository: localaiLicense: llama3.1

sekhmet_aleph-l3.1-8b-v0.1-i1
The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks. Model developer: Meta Model Architecture: Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Repository: localaiLicense: llama3.1

llama-guard-3-8b
Llama Guard 3 is a Llama-3.1-8B pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM – it generates text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated. Llama Guard 3 was aligned to safeguard against the MLCommons standardized hazards taxonomy and designed to support Llama 3.1 capabilities. Specifically, it provides content moderation in 8 languages, and was optimized to support safety and security for search and code interpreter tool calls.

Repository: localaiLicense: llama3.1

krutrim-ai-labs_krutrim-2-instruct
Krutrim-2 is a 12B parameter language model developed by the OLA Krutrim team. It is built on the Mistral-NeMo 12B architecture and trained across various domains, including web data, code, math, Indic languages, Indian context data, synthetic data, and books. Following pretraining, the model was finetuned for instruction following on diverse data covering a wide range of tasks, including knowledge recall, math, reasoning, coding, safety, and creative writing.

Repository: localaiLicense: krutrim-community-license-agreement-version-1.0

shieldstral-1.0-3b
Shieldstral 1.0 3B is Mistral AI's compact, policy-adaptive multimodal safety classifier. It evaluates text, images, or combined inputs against a natural-language safety policy and answers yes or no. The model supports twelve languages and a recommended context length of up to 32K tokens. This entry uses the Q4_K_M GGUF quantization and includes the Pixtral vision projector.

Repository: localaiLicense: apache-2.0

shieldstral-1.0-3b-q8
Shieldstral 1.0 3B is Mistral AI's compact, policy-adaptive multimodal safety classifier. This higher-quality variant uses the Q8_0 GGUF quantization and includes the Pixtral vision projector.

Repository: localaiLicense: apache-2.0

cydonia-24b-v4.2.0-i1
**Cydonia-24B-v4.2.0** is a creatively oriented, large language model developed by *TheDrummer*, based on the **Mistral-Small-3.2-24B-Instruct-2507** foundation. Fine-tuned for dynamic storytelling, imaginative writing, and expressive roleplay, it excels in narrative coherence, linguistic flair, and non-aligned, open-ended interaction. Designed for users seeking creativity over strict alignment, the model delivers rich, engaging, and often surprising outputs—ideal for fiction writing, worldbuilding, and entertainment-focused AI use. **Key Features:** - Built on Mistral-Small-3.2-24B-Instruct-2507 base - Optimized for creative writing, roleplay, and narrative depth - Minimal alignment constraints for greater freedom and expression - Available in GGUF, EXL3, and iMatrix formats for local inference > *“This is the best model of yours I've tried yet… It writes superbly well.”* – User testimonial **Best For:** Writers, worldbuilders, and creators who value imagination, voice, and stylistic richness over rigid safety or factual accuracy. *Model Repository:* [TheDrummer/Cydonia-24B-v4.2.0](https://huggingface.co/TheDrummer/Cydonia-24B-v4.2.0)

Repository: localaiLicense: apache-2.0

qwen-sea-lion-v4-32b-it-i1
**Model Name:** Qwen-SEA-LION-v4-32B-IT **Base Model:** Qwen3-32B **Type:** Instruction-tuned Large Language Model (LLM) **Language Support:** 11 languages including English, Mandarin, Burmese, Indonesian, Malay, Filipino, Tamil, Thai, Vietnamese, Khmer, and Lao **Context Length:** 128,000 tokens **Repository:** [aisingapore/Qwen-SEA-LION-v4-32B-IT](https://huggingface.co/aisingapore/Qwen-SEA-LION-v4-32B-IT) **License:** [Qwen Terms of Service](https://qwen.ai/termsservice) / [Qwen Usage Policy](https://qwen.ai/usagepolicy) **Overview:** Qwen-SEA-LION-v4-32B-IT is a high-performance, multilingual instruction-tuned LLM developed by AI Singapore, specifically optimized for Southeast Asia (SEA). Built on the Qwen3-32B foundation, it underwent continued pre-training on 100B tokens from the SEA-Pile v2 corpus and further fine-tuned on ~8 million question-answer pairs to enhance instruction-following and reasoning. Designed for real-world multilingual applications across government, education, and business sectors in Southeast Asia, it delivers strong performance in dialogue, content generation, and cross-lingual tasks. **Key Features:** - Trained for 11 major SEA languages with high linguistic accuracy - 128K token context for long-form content and complex reasoning - Optimized for instruction following, multi-turn dialogue, and cultural relevance - Available in full precision and quantized variants (4-bit/8-bit) - Not safety-aligned — suitable for downstream safety fine-tuning **Use Cases:** - Multilingual chatbots and virtual assistants in SEA regions - Cross-lingual content generation and translation - Educational tools and public sector applications in Southeast Asia - Research and development in low-resource language modeling **Note:** This model is not safety-aligned. Use with caution and consider additional alignment measures for production deployment. **Contact:** [sealion@aisingapore.org](mailto:sealion@aisingapore.org) for inquiries.

Repository: localaiLicense: apache-2.0

simia-tau-sft-qwen3-8b
The **Simia-Tau-SFT-Qwen3-8B** is a fine-tuned version of the Qwen3-8B language model, developed by Simia-Agent and adapted for enhanced instruction-following capabilities. This model is optimized for dialogue and task-oriented interactions, making it highly effective for real-world applications requiring nuanced understanding and coherent responses. The model is available in multiple quantized formats (GGUF), including Q4_K_S, Q5_K_M, Q8_0, and others, enabling efficient deployment across devices with varying computational resources. These quantized versions maintain strong performance while reducing memory footprint and inference latency. While this repository hosts a quantized variant (specifically designed for GGUF-based inference via tools like llama.cpp), the original base model is **Qwen3-8B**, a large-scale open-source language model from Alibaba Cloud. The fine-tuning (SFT) process improves its alignment with human intent and enhances its ability to follow complex instructions. > 🔍 **Note**: This is a quantized version; for the full-precision base model, refer to [Simia-Agent/Simia-Tau-SFT-Qwen3-8B](https://huggingface.co/Simia-Agent/Simia-Tau-SFT-Qwen3-8B) on Hugging Face. **Use Case**: Ideal for chatbots, assistant systems, and interactive applications requiring strong reasoning, safety, and fluency. **Model Size**: 8B parameters (quantized for efficiency). **License**: See the original model's license (typically Apache 2.0 for Qwen series). 👉 Recommended for edge deployment with GGUF-compatible tools.

Repository: localaiLicense: apache-2.0

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