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parler-tts-mini-v0.1
Parler-TTS is a lightweight text-to-speech (TTS) model that can generate high-quality, natural sounding speech in the style of a given speaker (gender, pitch, speaking style, etc). It is a reproduction of work from the paper Natural language guidance of high-fidelity text-to-speech with synthetic annotations by Dan Lyth and Simon King, from Stability AI and Edinburgh University respectively.
Repository: localaiLicense: apache-2.0
cross-encoder
A cross-encoder model that can be used for reranking
Repository: localaiLicense: apache-2.0
yi-coder-9b-chat
Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters.
Key features:
Excelling in long-context understanding with a maximum context length of 128K tokens.
Supporting 52 major programming languages:
'java', 'markdown', 'python', 'php', 'javascript', 'c++', 'c#', 'c', 'typescript', 'html', 'go', 'java_server_pages', 'dart', 'objective-c', 'kotlin', 'tex', 'swift', 'ruby', 'sql', 'rust', 'css', 'yaml', 'matlab', 'lua', 'json', 'shell', 'visual_basic', 'scala', 'rmarkdown', 'pascal', 'fortran', 'haskell', 'assembly', 'perl', 'julia', 'cmake', 'groovy', 'ocaml', 'powershell', 'elixir', 'clojure', 'makefile', 'coffeescript', 'erlang', 'lisp', 'toml', 'batchfile', 'cobol', 'dockerfile', 'r', 'prolog', 'verilog'
For model details and benchmarks, see Yi-Coder blog and Yi-Coder README.
Repository: localaiLicense: apache-2.0
yi-coder-1.5b-chat
Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters.
Key features:
Excelling in long-context understanding with a maximum context length of 128K tokens.
Supporting 52 major programming languages:
'java', 'markdown', 'python', 'php', 'javascript', 'c++', 'c#', 'c', 'typescript', 'html', 'go', 'java_server_pages', 'dart', 'objective-c', 'kotlin', 'tex', 'swift', 'ruby', 'sql', 'rust', 'css', 'yaml', 'matlab', 'lua', 'json', 'shell', 'visual_basic', 'scala', 'rmarkdown', 'pascal', 'fortran', 'haskell', 'assembly', 'perl', 'julia', 'cmake', 'groovy', 'ocaml', 'powershell', 'elixir', 'clojure', 'makefile', 'coffeescript', 'erlang', 'lisp', 'toml', 'batchfile', 'cobol', 'dockerfile', 'r', 'prolog', 'verilog'
For model details and benchmarks, see Yi-Coder blog and Yi-Coder README.
Repository: localaiLicense: apache-2.0
yi-coder-1.5b
Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters.
Key features:
Excelling in long-context understanding with a maximum context length of 128K tokens.
Supporting 52 major programming languages:
'java', 'markdown', 'python', 'php', 'javascript', 'c++', 'c#', 'c', 'typescript', 'html', 'go', 'java_server_pages', 'dart', 'objective-c', 'kotlin', 'tex', 'swift', 'ruby', 'sql', 'rust', 'css', 'yaml', 'matlab', 'lua', 'json', 'shell', 'visual_basic', 'scala', 'rmarkdown', 'pascal', 'fortran', 'haskell', 'assembly', 'perl', 'julia', 'cmake', 'groovy', 'ocaml', 'powershell', 'elixir', 'clojure', 'makefile', 'coffeescript', 'erlang', 'lisp', 'toml', 'batchfile', 'cobol', 'dockerfile', 'r', 'prolog', 'verilog'
For model details and benchmarks, see Yi-Coder blog and Yi-Coder README.
Repository: localaiLicense: apache-2.0
yi-coder-9b
Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters.
Key features:
Excelling in long-context understanding with a maximum context length of 128K tokens.
Supporting 52 major programming languages:
'java', 'markdown', 'python', 'php', 'javascript', 'c++', 'c#', 'c', 'typescript', 'html', 'go', 'java_server_pages', 'dart', 'objective-c', 'kotlin', 'tex', 'swift', 'ruby', 'sql', 'rust', 'css', 'yaml', 'matlab', 'lua', 'json', 'shell', 'visual_basic', 'scala', 'rmarkdown', 'pascal', 'fortran', 'haskell', 'assembly', 'perl', 'julia', 'cmake', 'groovy', 'ocaml', 'powershell', 'elixir', 'clojure', 'makefile', 'coffeescript', 'erlang', 'lisp', 'toml', 'batchfile', 'cobol', 'dockerfile', 'r', 'prolog', 'verilog'
For model details and benchmarks, see Yi-Coder blog and Yi-Coder README.
Repository: localaiLicense: apache-2.0
all-MiniLM-L6-v2
This framework provides an easy method to compute dense vector representations for sentences, paragraphs, and images. The models are based on transformer networks like BERT / RoBERTa / XLM-RoBERTa etc. and achieve state-of-the-art performance in various tasks. Text is embedded in vector space such that similar text are closer and can efficiently be found using cosine similarity.
Repository: localai
dreamshaper
A text-to-image model that uses Stable Diffusion 1.5 to generate images from text prompts. This model is DreamShaper model by Lykon.
Repository: localaiLicense: other
stable-diffusion-3-medium
Stable Diffusion 3 Medium is a Multimodal Diffusion Transformer (MMDiT) text-to-image model that features greatly improved performance in image quality, typography, complex prompt understanding, and resource-efficiency.
Repository: localaiLicense: other
flux.1-dev
FLUX.1 [dev] is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. For more information, please read our blog post.
Key Features
Cutting-edge output quality, second only to our state-of-the-art model FLUX.1 [pro].
Competitive prompt following, matching the performance of closed source alternatives .
Trained using guidance distillation, making FLUX.1 [dev] more efficient.
Open weights to drive new scientific research, and empower artists to develop innovative workflows.
Generated outputs can be used for personal, scientific, and commercial purposes as described in the flux-1-dev-non-commercial-license.
Repository: localaiLicense: flux-1-dev-non-commercial-license
flux.1-schnell
FLUX.1 [schnell] is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. For more information, please read our blog post.
Key Features
Cutting-edge output quality and competitive prompt following, matching the performance of closed source alternatives.
Trained using latent adversarial diffusion distillation, FLUX.1 [schnell] can generate high-quality images in only 1 to 4 steps.
Released under the apache-2.0 licence, the model can be used for personal, scientific, and commercial purposes.
Repository: localaiLicense: apache-2
flux.1dev-abliteratedv2
The FLUX.1 [dev] Abliterated-v2 model is a modified version of FLUX.1 [dev] and a successor to FLUX.1 [dev] Abliterated. This version has undergone a process called unlearning, which removes the model's built-in refusal mechanism. This allows the model to respond to a wider range of prompts, including those that the original model might have deemed inappropriate or harmful.
The abliteration process involves identifying and isolating the specific components of the model responsible for refusal behavior and then modifying or ablating those components. This results in a model that is more flexible and responsive, while still maintaining the core capabilities of the original FLUX.1 [dev] model.
Repository: localaiLicense: flux-1-dev-non-commercial-license
flux.1-dev-ggml-q8_0
FLUX.1 [dev] is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. For more information, please read our blog post.
Key Features
Cutting-edge output quality, second only to our state-of-the-art model FLUX.1 [pro].
Competitive prompt following, matching the performance of closed source alternatives .
Trained using guidance distillation, making FLUX.1 [dev] more efficient.
Open weights to drive new scientific research, and empower artists to develop innovative workflows.
Generated outputs can be used for personal, scientific, and commercial purposes as described in the flux-1-dev-non-commercial-license.
Repository: localaiLicense: flux-1-dev-non-commercial-license
flux.1-dev-ggml-abliterated-v2-q8_0
FLUX.1 [dev] is an abliterated version of FLUX.1 [dev]
Repository: localaiLicense: flux-1-dev-non-commercial-license
flux.1-krea-dev-ggml
FLUX.1 Krea [dev] is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. For more information, please read our blog post and Krea's blog post.
Cutting-edge output quality, with a focus on aesthetic photography.
Competitive prompt following, matching the performance of closed source alternatives.
Trained using guidance distillation, making FLUX.1 Krea [dev] more efficient.
Open weights to drive new scientific research, and empower artists to develop innovative workflows.
Generated outputs can be used for personal, scientific, and commercial purposes, as described in the flux-1-dev-non-commercial-license.
Repository: localaiLicense: flux-1-dev-non-commercial-license
flux.1-krea-dev-ggml-q8_0
FLUX.1 Krea [dev] is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. For more information, please read our blog post and Krea's blog post.
Cutting-edge output quality, with a focus on aesthetic photography.
Competitive prompt following, matching the performance of closed source alternatives.
Trained using guidance distillation, making FLUX.1 Krea [dev] more efficient.
Open weights to drive new scientific research, and empower artists to develop innovative workflows.
Generated outputs can be used for personal, scientific, and commercial purposes, as described in the flux-1-dev-non-commercial-license.
Repository: localaiLicense: flux-1-dev-non-commercial-license
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
qwen3-4b-thinking-2507-gspo-easy
**Model Name:** Qwen3-4B-Thinking-2507-GSPO-Easy
**Base Model:** Qwen3-4B (by Alibaba Cloud)
**Fine-tuned With:** GRPO (Generalized Reward Policy Optimization)
**Framework:** Hugging Face TRL (Transformers Reinforcement Learning)
**License:** [MIT](https://huggingface.co/leonMW/Qwen3-4B-Thinking-2507-GSPO-Easy/blob/main/LICENSE)
---
### 📌 Description:
A fine-tuned 4-billion-parameter version of **Qwen3-4B**, optimized for **step-by-step reasoning and complex problem-solving** using **GRPO**, a reinforcement learning method designed to enhance mathematical and logical reasoning in language models.
This model excels in tasks requiring **structured thinking**, such as solving math problems, logical puzzles, and multi-step reasoning, making it ideal for applications in education, AI assistants, and reasoning benchmarks.
### 🔧 Key Features:
- Trained with **TRL 0.23.1** and **Transformers 4.57.1**
- Optimized for **high-quality reasoning output**
- Part of the **Qwen3-4B-Thinking** series, designed to simulate human-like thought processes
- Compatible with Hugging Face `transformers` and `pipeline` API
### 📚 Use Case:
Perfect for applications demanding **deep reasoning**, such as:
- AI tutoring systems
- Advanced chatbots with explanation capabilities
- Automated problem-solving in STEM domains
### 📌 Quick Start (Python):
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="leonMW/Qwen3-4B-Thinking-2507-GSPO-Easy", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
> ✅ **Note**: This is the **original, non-quantized base model**. Quantized versions (e.g., GGUF) are available separately under the same repository for efficient inference on consumer hardware.
---
🔗 **Model Page:** [https://huggingface.co/leonMW/Qwen3-4B-Thinking-2507-GSPO-Easy](https://huggingface.co/leonMW/Qwen3-4B-Thinking-2507-GSPO-Easy)
📝 **Training Details & Visualizations:** [WandB Dashboard](https://wandb.ai/leonwenderoth-tu-darmstadt/huggingface/runs/t42skrc7)
---
*Fine-tuned using GRPO — a method proven to boost mathematical reasoning in open language models. Cite: Shao et al., 2024 (arXiv:2402.03300)*
Repository: localaiLicense: apache-2.0
orca-agent-v0.1-i1
**Model Name:** Orca-Agent-v0.1
**Base Model:** Qwen3-14B
**Repository:** [Danau5tin/Orca-Agent-v0.1](https://huggingface.co/Danau5tin/Orca-Agent-v0.1)
**License:** Apache 2.0
**Use Case:** Multi-Agent Orchestration for Complex Code & System Tasks
---
### 🔍 **Overview**
Orca-Agent-v0.1 is a powerful **task orchestration agent** designed to manage complex, multi-step workflows—especially in code and system administration—without directly modifying code. Instead, it acts as a strategic planner that coordinates a team of specialized agents.
---
### 🛠️ **Key Features**
- **Intelligent Task Breakdown:** Analyzes user requests and decomposes them into focused subtasks.
- **Agent Coordination:** Dynamically dispatches:
- *Explorer agents* to understand the system state.
- *Coder agents* to implement changes with precise instructions.
- *Verifier agents* to validate results.
- **Context Management:** Maintains a persistent context store to track discoveries across steps.
- **High Performance:** Achieves **18.25% on TerminalBench** when paired with Qwen3-Coder-30B, nearing the performance of a 480B model.
---
### 📊 **Performance**
| Orchestrator | Subagent | Terminal Bench |
|--------------|----------|----------------|
| Orca-Agent-v0.1-14B | Qwen3-Coder-30B | **18.25%** |
| Qwen3-14B | Qwen3-Coder-30B | 7.0% |
> *Trained on 32x H100s using GRPO + curriculum learning, with full open-source training code available.*
---
### 📌 **Example Output**
```xml
agent_type: 'coder'
title: 'Attempt recovery using the identified backup file'
description: |
Move the backup file from /tmp/terraform_work/.terraform.tfstate.tmp to /infrastructure/recovered_state.json.
Verify file existence, size, and permissions (rw-r--r--).
max_turns: 10
context_refs: ['task_003']
```
---
### 📁 **Serving**
- ✅ **vLLM:** `vllm serve Danau5tin/Orca-Agent-v0.1`
- ✅ **SGLang:** `python -m sglang.launch_server --model-path Danau5tin/Orca-Agent-v0.1`
---
### 🌐 **Learn More**
- **Training & Code:** [GitHub - Orca-Agent-RL](https://github.com/Danau5tin/Orca-Agent-RL)
- **Orchestration Framework:** [multi-agent-coding-system](https://github.com/Danau5tin/multi-agent-coding-system)
---
> ✅ *Note: The model at `mradermacher/Orca-Agent-v0.1-i1-GGUF` is a quantized version of this original model. This description reflects the full, unquantized version by the original author.*
Repository: localaiLicense: apache-2.0