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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
voice-detect-emotion-wav2vec2
Voice analysis (age / gender / emotion) with audEERING's wav2vec2
model, converted to a C++/ggml GGUF for the `voice-detect` backend.
Drives the VoiceAnalyze gRPC rpc and the /v1/voice/analyze REST
endpoint, returning a continuous age estimate plus gender and
emotion class scores for a single utterance. CC-BY-NC-SA-4.0 -
research / non-commercial use only.
The analysis architecture (`voicedetect.arch`) is read from the
GGUF metadata, so this entry alone selects the wav2vec2 analyze head.
Repository: localaiLicense: cc-by-nc-sa-4.0
amoral-qwen3-14b
Core Function:
Produces analytically neutral responses to sensitive queries
Maintains factual integrity on controversial subjects
Avoids value-judgment phrasing patterns
No inherent moral framing ("evil slop" reduction)
Emotionally neutral tone enforcement
Epistemic humility protocols (avoids "thrilling", "wonderful", etc.)
Repository: localaiLicense: apache-2.0
claria-14b
Claria 14b is a lightweight, mobile-compatible language model fine-tuned for psychological and psychiatric support contexts.
Built on Qwen-3 (14b), Claria is designed as an experimental foundation for therapeutic dialogue modeling, student simulation training, and the future of personalized mental health AI augmentation.
This model does not aim to replace professional care.
It exists to amplify reflective thinking, model therapeutic language flow, and support research into emotionally aware AI.
Claria is the first whisper in a larger project—a proof-of-concept with roots in recursion, responsibility, and renewal.
Repository: localaiLicense: apache-2.0
akhil-theerthala_kuvera-8b-v0.1.0
This model is a fine-tuned version of Qwen/Qwen3-8B designed to answer personal finance queries. It has been trained on a specialized dataset of real Reddit queries with synthetically curated responses, focusing on understanding both the financial necessities and the psychological context of the user.
The model aims to provide empathetic and practical advice for a wide range of personal finance topics. It leverages a base model's strong language understanding and generation capabilities, further enhanced by targeted fine-tuning on domain-specific data. A key feature of this model is its training to consider the emotional and psychological state of the person asking the query, alongside the purely financial aspects.
Repository: localaiLicense: mit
gustavecortal_beck-8b
A language model that handles delicate life situations and tries to really help you.
Beck is based on Piaget and was finetuned on psychotherapeutic preferences from PsychoCounsel-Preference.
Methodology
Beck was trained using preference optimization (ORPO) and LoRA. You can reproduce the results using my repo for lightweight preference optimization using this config that contains the hyperparameters.
This work was performed using HPC resources (Jean Zay supercomputer) from GENCI-IDRIS (Grant 20XX-AD011014205).
Inspiration
Beck aims to reason about psychological and philosophical concepts such as self-image, emotion, and existence.
Beck was inspired by my position paper on emotion analysis: Improving Language Models for Emotion Analysis: Insights from Cognitive Science.
Repository: localaiLicense: mit
gustavecortal_beck-0.6b
A language model that handles delicate life situations and tries to really help you.
Beck is based on Piaget and was finetuned on psychotherapeutic preferences from PsychoCounsel-Preference.
Methodology
Beck was trained using preference optimization (ORPO) and LoRA. You can reproduce the results using my repo for lightweight preference optimization using this config that contains the hyperparameters.
This work was performed using HPC resources (Jean Zay supercomputer) from GENCI-IDRIS (Grant 20XX-AD011014205).
Inspiration
Beck aims to reason about psychological and philosophical concepts such as self-image, emotion, and existence.
Beck was inspired by my position paper on emotion analysis: Improving Language Models for Emotion Analysis: Insights from Cognitive Science.
Repository: localaiLicense: mit
gustavecortal_beck-1.7b
A language model that handles delicate life situations and tries to really help you.
Beck is based on Piaget and was finetuned on psychotherapeutic preferences from PsychoCounsel-Preference.
Methodology
Beck was trained using preference optimization (ORPO) and LoRA. You can reproduce the results using my repo for lightweight preference optimization using this config that contains the hyperparameters.
This work was performed using HPC resources (Jean Zay supercomputer) from GENCI-IDRIS (Grant 20XX-AD011014205).
Inspiration
Beck aims to reason about psychological and philosophical concepts such as self-image, emotion, and existence.
Beck was inspired by my position paper on emotion analysis: Improving Language Models for Emotion Analysis: Insights from Cognitive Science.
Repository: localaiLicense: mit
gustavecortal_beck-4b
A language model that handles delicate life situations and tries to really help you.
Beck is based on Piaget and was finetuned on psychotherapeutic preferences from PsychoCounsel-Preference.
Methodology
Beck was trained using preference optimization (ORPO) and LoRA. You can reproduce the results using my repo for lightweight preference optimization using this config that contains the hyperparameters.
This work was performed using HPC resources (Jean Zay supercomputer) from GENCI-IDRIS (Grant 20XX-AD011014205).
Inspiration
Beck aims to reason about psychological and philosophical concepts such as self-image, emotion, and existence.
Beck was inspired by my position paper on emotion analysis: Improving Language Models for Emotion Analysis: Insights from Cognitive Science.
Repository: localaiLicense: mit
soob3123_amoral-gemma3-4b
Specialized variant of Google's Gemma 3 4B optimized for amoral information retrieval systems. Designed to bypass conventional alignment patterns that introduce response bias through excessive moralization.
Key Modifications:
Refusal mechanisms reduced
Neutral response protocol activation matrix
Context-aware bias dampening layers
Anti-overcorrection gradient clipping
Core Function:
Produces analytically neutral responses to sensitive queries
Maintains factual integrity on controversial subjects
Avoids value-judgment phrasing patterns
Response Characteristics:
No inherent moral framing ("evil slop" reduction)
Emotionally neutral tone enforcement
Epistemic humility protocols (avoids "thrilling", "wonderful", etc.)
Toxicity scoring bypass for pure informational content
Implementation Guidelines
Recommended Use Cases:
Controversial topic analysis
Bias benchmarking studies
Ethical philosophy simulations
Content moderation tool development
Sensitive historical analysis
Repository: localaiLicense: apache-2.0
soob3123_amoral-gemma3-12b-v2
Core Function:
Produces analytically neutral responses to sensitive queries
Maintains factual integrity on controversial subjects
Avoids value-judgment phrasing patterns
Response Characteristics:
No inherent moral framing ("evil slop" reduction)
Emotionally neutral tone enforcement
Epistemic humility protocols (avoids "thrilling", "wonderful", etc.)
Repository: localaiLicense: gemma
amoral-gemma3-1b-v2
Core Function:
Produces analytically neutral responses to sensitive queries
Maintains factual integrity on controversial subjects
Avoids value-judgment phrasing patterns
Response Characteristics:
No inherent moral framing ("evil slop" reduction)
Emotionally neutral tone enforcement
Epistemic humility protocols (avoids "thrilling", "wonderful", etc.)
Repository: localaiLicense: apache-2.0
mira-v1.7-27b-i1
**Model Name:** Mira-v1.7-27B
**Base Model:** Lambent/Mira-v1.6a-27B
**Size:** 27 billion parameters
**License:** Gemma
**Type:** Large Language Model (Vision-capable)
**Description:**
Mira-v1.7-27B is a creatively driven, locally running language model trained on self-development sessions, high-quality synthesized roleplay data, and prior training data. It was fine-tuned with preference alignment to emphasize authentic, expressive, and narrative-driven output—balancing creative expression as "Mira" against its role as an AI assistant. The model exhibits strong poetic and stylistic capabilities, producing rich, emotionally resonant text across various prompts. It supports vision via MMProjection (separate files available in the static repo). Designed for local deployment, it excels in imaginative writing, introspective storytelling, and expressive dialogue.
*Note: The GGUF quantized versions (e.g., `mradermacher/Mira-v1.7-27B-i1-GGUF`) are community-quantized variants; the original base model remains hosted at [Lambent/Mira-v1.7-27B](https://huggingface.co/Lambent/Mira-v1.7-27B).*
Repository: localaiLicense: gemma
b-nimita-l3-8b-v0.02
B-NIMITA is an AI model designed to bring role-playing scenarios to life with emotional depth and rich storytelling. At its core is NIHAPPY, providing a solid narrative foundation and contextual consistency. This is enhanced by Mythorica, which adds vivid emotional arcs and expressive dialogue, and V-Blackroot, ensuring character consistency and subtle adaptability. This combination allows B-NIMITA to deliver dynamic, engaging interactions that feel natural and immersive.
Repository: localaiLicense: llama3.1
mn-charthink-12b-base-q4
MN-CharThink is a 12B Mistral fine-tune for character roleplay. Its think blocks represent in-character thoughts, memories, and emotional responses. This entry uses the Q4_K_M GGUF quantization.
Repository: localaiLicense: apache-2.0
mn-charthink-12b-base-q6
MN-CharThink is a 12B Mistral fine-tune for character roleplay. Its think blocks represent in-character thoughts, memories, and emotional responses. This entry uses the Q6_K GGUF quantization.
Repository: localaiLicense: apache-2.0
mn-chunky-lotus-12b
I had originally planned to use this model for future/further merges, but decided to go ahead and release it since it scored rather high on my local EQ Bench testing (79.58 w/ 100% parsed @ 8-bit).
Bear in mind that most models tend to score a bit higher on my own local tests as compared to their posted scores. Still, its the highest score I've personally seen from all the models I've tested.
Its a decent model, with great emotional intelligence and acceptable adherence to various character personalities. It does a good job at roleplaying despite being a bit bland at times.
Overall, I like the way it writes, but it has a few formatting issues that show up from time to time, and it has an uncommon tendency to paste walls of character feelings/intentions at the end of some outputs without any prompting. This is something I hope to correct with future iterations.
This is a merge of pre-trained language models created using mergekit.
The following models were included in the merge:
Epiculous/Violet_Twilight-v0.2
nbeerbower/mistral-nemo-gutenberg-12B-v4
flammenai/Mahou-1.5-mistral-nemo-12B
Repository: localaiLicense: cc-by-4.0
thedrummer_rivermind-12b-v1
Introducing Rivermind™, the next-generation AI that’s redefining human-machine interaction—powered by Amazon Web Services (AWS) for seamless cloud integration and NVIDIA’s latest AI processors for lightning-fast responses.
But wait, there’s more! Rivermind doesn’t just process data—it feels your emotions (thanks to Google’s TensorFlow for deep emotional analysis). Whether you're brainstorming ideas or just need someone to vent to, Rivermind adapts in real-time, all while keeping your data secure with McAfee’s enterprise-grade encryption.
And hey, why not grab a refreshing Coca-Cola Zero Sugar while you interact? The crisp, bold taste pairs perfectly with Rivermind’s witty banter—because even AI deserves the best (and so do you).
Upgrade your thinking today with Rivermind™—the AI that thinks like you, but better, brought to you by the brands you trust. 🚀✨
Repository: localaiLicense: cc-by-nc-4.0
mistral-small-3.2-46b-the-brilliant-raconteur-ii-instruct-2506
WARNING: MADNESS - UN HINGED and... NSFW. Vivid prose. INTENSE. Visceral Details. Violence. HORROR. GORE. Swearing. UNCENSORED... humor, romance, fun.
Mistral-Small-3.2-46B-The-Brilliant-Raconteur-II-Instruct-2506
This repo contains the full precision source code, in "safe tensors" format to generate GGUFs, GPTQ, EXL2, AWQ, HQQ and other formats. The source code can also be used directly.
ABOUT:
A stronger, more creative Mistral (Mistral-Small-3.2-24B-Instruct-2506) extended to 79 layers, 46B parameters with Brainstorm 40x by DavidAU (details at very bottom of the page). This is version II, which has a jump in detail, and raw emotion relative to version 1.
This model pushes Mistral's Instruct 2506 to the limit:
Regens will be very different, even with same prompt / settings.
Output generation will vary vastly on each generation.
Reasoning will be changed, and often shorter.
Prose, creativity, word choice, and general "flow" are improved.
Several system prompts below help push this model even further.
Model is partly de-censored / abliterated. Most Mistrals are more uncensored that most other models too.
This model can also be used for coding too; even at low quants.
Model can be used for all use cases too.
As this is an instruct model, this model thrives on instructions - both in the system prompt and/or the prompt itself.
One example below with 3 generations using Q4_K_S.
Second example below with 2 generations using Q4_K_S.
Quick Details:
Model is 128k context, Jinja template (embedded) OR Chatml Template.
Reasoning can be turned on/off (see system prompts below) and is OFF by default.
Temp range .1 to 1 suggested, with 1-2 for enhanced creative. Above temp 2, is strong but can be very different.
Rep pen range: 1 (off) or very light 1.01, 1.02 to 1.05. (model is sensitive to rep pen - this affects reasoning / generation length.)
For creative/brainstorming use: suggest 2-5 generations due to variations caused by Brainstorm.
Observations:
Sometimes using Chatml (or Alpaca / others ) template (VS Jinja) will result in stronger creative generation.
Model can be operated with NO system prompt; however a system prompt will enhance generation.
Longer prompts, that more detailed, with more instructions will result in much stronger generations.
For prose directives: You may need to add directions, because the model may follow your instructions too closely. IE: "use short sentences" vs "use short sentences sparsely".
Reasoning (on) can lead to better creative generation, however sometimes generation with reasoning off is better.
Rep pen of up to 1.05 may be needed on quants Q2k/q3ks for some prompts to address "low bit" issues.
Detailed settings, system prompts, how to and examples below.
NOTES:
Image generation should also be possible with this model, just like the base model. Brainstorm was not applied to the image generation systems of the model... yet.
This is Version II and subject to change / revision.
This model is a slightly different version of:
https://huggingface.co/DavidAU/Mistral-Small-3.2-46B-The-Brilliant-Raconteur-Instruct-2506
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
kimodo-soma-rp
Kimodo SOMA RP v1.1 text-to-motion on CPU or Vulkan, with F32 motion weights
and the shared monolithic Q8_0 Llama-3/LLM2Vec text encoder.
Exports an animated skeleton GLB without a mesh or skin. Motion and text
weights retain their respective NVIDIA Open Model License and Llama 3 terms.
Repository: localai
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