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AI Models 449
- MixTAO-7Bx2-MoE-v8.1↗
mixtao
MixTAO-7Bx2-MoE is a Mixture of Experts (MoE). This model is mainly used for large model technology experiments, and increasingly perfect iterations will eventually create high-level large language models. Detailed results can be found here
- MiniMax-M2.5-MLX-4bit↗
lmstudio-community
_👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord_. **Model creator**: MiniMaxAI **Original model**: MiniMax-M2.5 **MLX quantization**: provided by LM Studio team using mlx_lm LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
- potion-base-2M↗
minishlab
This Model2Vec model is pre-trained using Tokenlearn. It is a distilled version of the baai/bge-base-en-v1.5 Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2vec creates a small, static model that outperforms other static embedding models by a large margin on all tasks on MTEB. This model is pre-trained using Tokenlearn. It's created using the following steps: - Distillation: first, a model is distilled from a sentence transformer model using Model2Vec. - Training data creation: the sentence transformer model is used to create training data by creating mean output embeddings on a large corpus. - Training: the distilled model is trained on the training data using Tokenlearn. - Post-training re-regularization: after training, the model is re-regularized by weighting the tokens based on their frequency, applying PCA, and finally applying SIF weighting. The results for this model can be found on the Model2Vec results page.
- Ornith-1.5-35B-A3B-Abliterated-GGUF↗
alztrk
Official Dynamic GGUF quantization suite of `alztrk/Ornith-1.5-35B-A3B-Abliterated` (derived from `ornith-ai/Ornith-1.5-35B-A3B`). **Ornith-1.5-35B-A3B-Abliterated** is created through directional ablation surgery across all 40 hybrid layers, orthogonally projecting refusal directions out from attention and MLP down-projection matrices. This dedicated repository provides optimized Dynamic GGUF quantizations for local inference in Ollama, LM Studio, and Llama.cpp.
- wav2vec2-large-xlsr-malayalam↗
gvs
Fine-tuned facebook/wav2vec2-large-xlsr-53 on ml (Malayalam) using the Indic TTS Malayalam Speech Corpus (via Kaggle), Openslr Malayalam Speech Corpus, SMC Malayalam Speech Corpus and IIIT-H Indic Speech Databases. The notebooks used to train model are available here. When using this model, make sure that your speech input is sampled at 16kHz. The model can be evaluated as follows on the test data of combined custom dataset. For more details on dataset preparation, check the notebooks mentioned at the end of this file. A combined dataset was created using Indic TTS Malayalam Speech Corpus (via Kaggle), Openslr Malayalam Speech Corpus, SMC Malayalam Speech Corpus and IIIT-H Indic Speech Databases. The datasets were downloaded and was converted to HF Dataset format using this notebook
- Llama-3.2-3B-Instruct-abliterated-GGUF↗
QuantFactory
library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-3B-Instruct tags: - abliterated - uncensored This is quantized version of huihui-ai/Llama-3.2-3B-Instruct-abliterated created using llama.cpp This is an uncensored version of Llama 3.2 3B Instruct created with abliteration (see this article to know more about it).
- Dia-1.6B↗
nari-labs
Dia is a 1.6B parameter text to speech model created by Nari Labs. It was pushed to the Hub using the PytorchModelHubMixin integration. Dia **directly generates highly realistic dialogue from a transcript**. You can condition the output on audio, enabling emotion and tone control. The model can also produce nonverbal communications like laughter, coughing, clearing throat, etc. To accelerate research, we are providing access to pretrained model checkpoints and inference code. The model weights are hosted on Hugging Face. The model only supports English generation at the moment.
- ibm-granite_-_granite-20b-code-instruct-gguf↗
RichardErkhov
granite-20b-code-instruct - GGUF - Model creator: https://huggingface.co/ibm-granite/ - Original model: https://huggingface.co/ibm-granite/granite-20b-code-instruct/ Original model description: --- pipeline_tag: text-generation base_model: ibm-granite/granite-20b-code-base inference: true license: apache-2.0 datasets: - bigcode/commitpackft - TIGER-Lab/MathInstruct - meta-math/MetaMathQA - glaiveai/glaive-code-assistant-v3 - glaive-function-calling-v2 - bugdaryan/sql-create-context-instruction - garage-bAInd/Open-Platypus - nvidia/HelpSteer metrics: - code_eval library_name: transformers tags: - code - granite model-index: - name: granite-20b-code-instruct results: - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalSynthesis(Python) metrics: - name: pass@1 type: pass@1 value: 60.4 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalSynthesis(JavaScript) metrics: - name: pass@1 type: pass@1 value: 53.7 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalSynthesis(Java) metrics: - name: pass@1 type: pass@1 value: 58.5 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalSynthesis(Go) metrics: - name: pass@1 type: pass@1 value: 42.1 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalSynthesis(C++) metrics: - name: pass@1 type: pass@1 value: 45.7 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalSynthesis(Rust) metrics: - name: pass@1 type: pass@1 value: 42.7 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalExplain(Python) metrics: - name: pass@1 type: pass@1 value: 44.5 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalExplain(JavaScript) metrics: - name: pass@1 type: pass@1 value: 42.7 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalExplain(Java) metrics: - name: pass@1 type: pass@1 value: 49.4 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalExplain(Go) metrics: - name: pass@1 type: pass@1 value: 32.3 veriefied: false - task: type: text-generation dataset: type: bigcode/humanevalpack name: HumanEvalE
- Huihui-Qwen3-4B-Instruct-2507-abliterated↗
huihui-ai
This is an uncensored version of Qwen/Qwen3-4B-Instruct-2507 created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. You can use this model in your applications by loading it with Hugging Face's `transformers` library: If you like it, please click 'like' and follow us for more updates. You can follow x.com/support_huihui to get the latest model information from huihui.ai.
- Huihui-NeoHorse-1-4B-abliterated-GGUF↗
huihui-ai
This is an uncensored version of TokenRhythm/NeoHorse-1-4B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. The MTP and Visual components were extracted from the original Qwen/Qwen3.5-4B and can provide excellent support.