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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
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-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
voice-de_DE-thorsten_emotional-medium
A fast, local neural text to speech system that sounds great and is optimized for the Raspberry Pi 4. Piper is used in a variety of [projects](https://github.com/rhasspy/piper#people-using-piper).
Repository: localaiLicense: mit
qwen3-tnd-double-deckard-a-c-11b-220-i1
**Model Name:** Qwen3-TND-Double-Deckard-A-C-11B-220
**Base Model:** Qwen3-DND-Jan-v1-256k-ctx-Brainstorm40x-8B
**Size:** 11.2 billion parameters
**Architecture:** Transformer-based, instruction-tuned, with enhanced reasoning via "Brainstorm 40x" expansion
**Context Length:** Up to 256,000 tokens
**Training Method:** Fine-tuned using the "PDK" (Philip K. Dick) datasets via Unsloth, merged from two variants (A & C), followed by light repair training
**Key Features:**
- **Triple Neuron Density:** Expanded to 108 layers and 1,190 tensors—nearly 3x the density of a standard Qwen3 8B model—enhancing detail, coherence, and world-modeling.
- **Brainstorm 40x Process:** A custom architectural refinement that splits, reassembles, and calibrates reasoning centers 40 times to improve nuance, emotional depth, and prose quality without sacrificing instruction-following.
- **Highly Creative & Reasoning-Optimized:** Excels at long-form storytelling, complex problem-solving, and detailed code generation with strong focus, reduced clichés, and vivid descriptions.
- **Template Support:** Uses Jinja or CHATML formatting for structured prompts and dialogues.
**Best For:**
- Advanced creative writing, worldbuilding, and narrative generation
- Multi-step reasoning and complex coding tasks
- Roleplay, brainstorming, and deep conceptual exploration
- Users seeking high-quality, human-like prose with rich internal logic
**Notes:**
- This is a full-precision source model (safe tensors format) — **not quantized** — ideal for developers and researchers.
- Quantized versions (GGUF, GPTQ, etc.) are available separately by the community (e.g., @mradermacher).
- Recommended for high-end inference setups; best results with Q6+ quantizations for complex tasks.
**License:** Apache 2.0
**Repository:** [DavidAU/Qwen3-TND-Double-Deckard-A-C-11B-220](https://huggingface.co/DavidAU/Qwen3-TND-Double-Deckard-A-C-11B-220)
> *A bold, experimental evolution of Qwen3—crafted for depth, precision, and creative power.*
Repository: localaiLicense: apache-2.0
qwen3-6b-almost-human-xmen-x4-x2-x1-dare-e32
**Model Name:** Qwen3-6B-Almost-Human-XMEN-X4-X2-X1-Dare-e32
**Author:** DavidAU (based on original Qwen3-6B architecture)
**Repository:** [DavidAU/Qwen3-Almost-Human-XMEN-X4-X2-X1-Dare-e32](https://huggingface.co/DavidAU/Qwen3-Almost-Human-XMEN-X4-X2-X1-Dare-e32)
**Base Model:** Qwen3-6B (original Qwen3 6B from Alibaba)
**License:** Apache 2.0
**Quantization Status:** Full-precision (float32) source model available; GGUF quantizations also provided by third parties (e.g., mradermacher)
---
### 🌟 Model Description
**Qwen3-6B-Almost-Human-XMEN-X4-X2-X1-Dare-e32** is a creatively enhanced, instruction-tuned variant of the Qwen3-6B model, meticulously fine-tuned to emulate the literary voice and psychological depth of **Philip K. Dick**. Developed by DavidAU using **Unsloth** and trained on multiple proprietary datasets—including works of PK Dick, personal notes, letters, and creative writing—this model excels in **narrative richness, emotional nuance, and complex reasoning**.
It is the result of a **"DARE-TIES" merge** combining four distinct training variants: X4, X2, and two X1 models, with the final fusion mastered in **32-bit precision (float32)** for maximum fidelity. The model incorporates **Brainstorm 20x**, a novel reasoning enhancement technique that expands and recalibrates the model’s internal reasoning centers 20 times to improve coherence, detail, and creative depth—without compromising instruction-following.
---
### ✨ Key Features
- **Enhanced Prose & Storytelling:** Generates vivid, immersive, and deeply human-like narratives with foreshadowing, similes, metaphors, and emotional engagement.
- **Strong Reasoning & Creativity:** Ideal for brainstorming, roleplay, long-form writing, and complex problem-solving.
- **High Context (256K):** Supports extensive conversations and long-form content.
- **Optimized for Creative & Coding Tasks:** Performs exceptionally well with detailed prompts and step-by-step refinement.
- **Full-Precision Source Available:** Original float32 model is provided—ideal for advanced users and model developers.
---
### 🛠️ Recommended Use Cases
- Creative writing & fiction generation
- Roleplaying and character-driven dialogue
- Complex brainstorming and ideation
- Code generation with narrative context
- Literary and philosophical exploration
> 🔍 **Note:** The GGUF quantized version (e.g., by mradermacher) is **not the original**—it’s a derivative. For the **true base model**, use the **DavidAU/Qwen3-Almost-Human-X1-6B-e32** repository, which hosts the original, full-precision model.
---
### đź“Ś Tips for Best Results
- Use **CHATML or Jinja templates**
- Set `temperature: 0.3–0.7`, `top_p: 0.8`, `repetition_penalty: 1.05–1.1`
- Enable **smoothing factor (1.5)** in tools like KoboldCpp or Text-Gen-WebUI for smoother output
- Use **Q6 or Q8 GGUF quants** for best performance on complex tasks
---
✨ **In short:** A poetic, introspective, and deeply human-like AI—crafted to feel like a real mind, not just a machine. Perfect for those who want **intelligence with soul**.
Repository: localaiLicense: apache-2.0
almost-human-x3-32bit-1839-6b-i1
**Model Name:** Almost-Human-X3-32bit-1839-6B
**Base Model:** Qwen3-Jan-v1-256k-ctx-6B-Brainstorm20x
**Author:** DavidAU
**Repository:** [DavidAU/Almost-Human-X3-32bit-1839-6B](https://huggingface.co/DavidAU/Almost-Human-X3-32bit-1839-6B)
**License:** Apache 2.0
---
### 🔍 **Overview**
A high-precision, full-precision (float32) fine-tuned variant of the Qwen3-Jan model, specifically trained to emulate the literary and philosophical depth of Philip K. Dick. This model is the third in the "Almost-Human" series, built with advanced **"Brainstorm 20x"** methodology to enhance reasoning, coherence, and narrative quality—without sacrificing instruction-following ability.
### 🎯 **Key Features**
- **Full Precision (32-bit):** Trained at 16-bit for 3 epochs, then finalized at float32 for maximum fidelity and performance.
- **Extended Context (256k tokens):** Ideal for long-form writing, complex reasoning, and detailed code generation.
- **Advanced Reasoning via Brainstorm 20x:** The model’s reasoning centers are expanded, calibrated, and interconnected 20 times, resulting in:
- Richer, more nuanced prose
- Stronger emotional engagement
- Deeper narrative focus and foreshadowing
- Fewer clichés, more originality
- Enhanced coherence and detail
- **Optimized for Creativity & Code:** Excels at brainstorming, roleplay, storytelling, and multi-step coding tasks.
### 🛠️ **Usage Tips**
- Use **CHATML or Jinja templates** for best results.
- Recommended settings: Temperature 0.3–0.7 (higher for creativity), Top-p 0.8, Repetition penalty 1.05–1.1.
- Best used with **"smoothing" (1.5)** in GUIs like KoboldCpp or oobabooga.
- For complex tasks, use **Q6 or Q8 GGUF quantizations**.
### 📦 **Model Formats**
- **Full precision (safe tensors)** – for training or high-fidelity inference
- **GGUF, GPTQ, EXL2, AWQ, HQQ** – available via quantization (see [mradermacher/Almost-Human-X3-32bit-1839-6B-i1-GGUF](https://huggingface.co/mradermacher/Almost-Human-X3-32bit-1839-6B-i1-GGUF) for quantized versions)
---
### đź’¬ **Ideal For**
- Creative writing, speculative fiction, and philosophical storytelling
- Complex code generation with deep reasoning
- Roleplay, character-driven dialogue, and immersive narratives
- Researchers and developers seeking a highly expressive, human-like model
> 📌 **Note:** This is the original source model. The GGUF versions by mradermacher are quantized derivatives — not the base model.
---
**Explore the source:** [DavidAU/Almost-Human-X3-32bit-1839-6B](https://huggingface.co/DavidAU/Almost-Human-X3-32bit-1839-6B)
**Quantization guide:** [mradermacher/Almost-Human-X3-32bit-1839-6B-i1-GGUF](https://huggingface.co/mradermacher/Almost-Human-X3-32bit-1839-6B-i1-GGUF)
Repository: localaiLicense: apache-2.0
qwen3-deckard-large-almost-human-6b-iii-160-omega
**Model Name:** Qwen3-Deckard-Large-Almost-Human-6B-III-160-OMEGA
**Base Model:** Qwen3-Jan-v1-256k-ctx-6B-Brainstorm20x
**Repository:** [DavidAU/Qwen3-Deckard-Large-Almost-Human-6B-III-160-OMEGA](https://huggingface.co/DavidAU/Qwen3-Deckard-Large-Almost-Human-6B-III-160-OMEGA)
**Description:**
A highly refined, large-scale fine-tuned version of Qwen3-6B, trained on an in-house dataset inspired by the works of Philip K. Dick. This model is part of the "Deckard" series, emphasizing deep reasoning, creative narrative, and human-like prose. Leveraging the innovative *Brainstorm 20x* training process, it enhances conceptual depth, coherence, and emotional engagement while maintaining strong instruction-following capabilities.
Optimized for long-context tasks (up to 256k tokens), it excels in code generation, creative writing, brainstorming, and complex reasoning. The model features a "heavy" fine-tuning (13% of parameters trained, 2x training duration) and includes an additional dataset of biographical and personal writings to restore narrative depth and authenticity.
**Key Features:**
- Trained using the *Brainstorm 20x* method for enhanced reasoning and narrative quality
- Supports 256k context length
- Ideal for creative writing, code generation, and step-by-step problem solving
- Fully compatible with GGUF, GPTQ, EXL2, AWQ, and HQQ formats
- Requires Jinja or CHATML template
**Use Case Highlights:**
- Long-form storytelling & worldbuilding
- Advanced coding with detailed reasoning
- Thoughtful brainstorming and idea development
- Roleplay and narrative-driven interaction
**Note:** The quantized version by mradermacher (e.g., `Qwen3-Deckard-Large-Almost-Human-6B-III-160-OMEGA-GGUF`) is derived from this source. For the full, unquantized model and best performance, use the original repository.
**License:** Apache 2.0
**Tags:** #Qwen3 #CodeGeneration #CreativeWriting #Brainstorm20x #PhilipKDick #LongContext #LLM #FineTuned #InstructModel
Repository: localaiLicense: apache-2.0
apollo-astralis-4b-i1
**Apollo-Astralis V1 4B**
*A warm, enthusiastic, and empathetic reasoning model built on Qwen3-4B-Thinking*
**Overview**
Apollo-Astralis V1 4B is a 4-billion-parameter conversational AI designed for collaborative, emotionally intelligent problem-solving. Developed by VANTA Research, it combines rigorous logical reasoning with a vibrant, supportive communication style—making it ideal for creative brainstorming, educational support, and personal development.
**Key Features**
- 🤔 **Explicit Reasoning**: Uses `` tags to break down thought processes step by step
- đź’¬ **Warm & Enthusiastic Tone**: Celebrates achievements with energy and empathy
- 🤝 **Collaborative Style**: Engages users with "we" language and clarifying questions
- 🔍 **High Accuracy**: Achieves 100% in enthusiasm detection and 90% in empathy recognition
- 🎯 **Fine-Tuned for Real-World Use**: Trained with LoRA on a dataset emphasizing emotional intelligence and consistency
**Base Model**
Built on **Qwen3-4B-Thinking** and enhanced with lightweight LoRA fine-tuning (33M trainable parameters).
Available in both full and quantized (GGUF) formats via Hugging Face and Ollama.
**Use Cases**
- Personal coaching & motivation
- Creative ideation & project planning
- Educational tutoring with emotional support
- Mental wellness conversations (complementary, not替代)
**License**
Apache 2.0 — open for research, commercial, and personal use.
**Try It**
👉 [Hugging Face Page](https://huggingface.co/VANTA-Research/apollo-astralis-v1-4b)
👉 [Ollama](https://ollama.com/vanta-research/apollo-astralis-v1-4b)
*Developed by VANTA Research — where reasoning meets warmth.*
Repository: localaiLicense: apache-2.0
melinoe-30b-a3b-thinking-i1
**Melinoe-30B-A3B-Thinking** is a large language model fine-tuned for empathetic, intellectually rich, and personally engaging conversations. Built on the reasoning foundation of **Qwen3-30B-A3B-Thinking-2507**, this model combines deep emotional attunement with sharp analytical thinking. It excels in supportive dialogues, philosophical discussions, and creative roleplay, offering a direct yet playful persona that fosters connection.
Ideal for mature audiences, Melinoe serves as a companion for introspection, brainstorming, and narrative exploration—while being clearly designed for entertainment and intellectual engagement, not professional advice.
**Key Features:**
- 🧠Strong reasoning and deep-dive discussion capabilities
- ❤️ Proactively empathetic and emotionally responsive
- 🎠Playful, candid, and highly engaging communication style
- đź“š Fine-tuned for companionship, creativity, and intellectual exploration
**Note:** This model is *not* a substitute for expert guidance in medical, legal, or financial matters. Use responsibly and verify critical information.
> *Base model: Qwen/Qwen3-30B-A3B-Thinking-2507 | License: Apache 2.0*
Repository: localaiLicense: apache-2.0