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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
voice-detect-age-gender-wav2vec2
wav2vec2-large-robust age + gender analysis head
(audeering/wav2vec2-large-robust-24-ft-age-gender), 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 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
mistralai_devstral-small-2505
Devstral is an agentic LLM for software engineering tasks built under a collaboration between Mistral AI and All Hands AI 🙌. Devstral excels at using tools to explore codebases, editing multiple files and power software engineering agents. The model achieves remarkable performance on SWE-bench which positionates it as the #1 open source model on this benchmark.
It is finetuned from Mistral-Small-3.1, therefore it has a long context window of up to 128k tokens. As a coding agent, Devstral is text-only and before fine-tuning from Mistral-Small-3.1 the vision encoder was removed.
For enterprises requiring specialized capabilities (increased context, domain-specific knowledge, etc.), we will release commercial models beyond what Mistral AI contributes to the community.
Learn more about Devstral in our blog post.
Key Features:
Agentic coding: Devstral is designed to excel at agentic coding tasks, making it a great choice for software engineering agents.
lightweight: with its compact size of just 24 billion parameters, Devstral is light enough to run on a single RTX 4090 or a Mac with 32GB RAM, making it an appropriate model for local deployment and on-device use.
Apache 2.0 License: Open license allowing usage and modification for both commercial and non-commercial purposes.
Context Window: A 128k context window.
Tokenizer: Utilizes a Tekken tokenizer with a 131k vocabulary size.
Repository: localaiLicense: apache-2.0
mistralai_magistral-small-2506
Building upon Mistral Small 3.1 (2503), with added reasoning capabilities, undergoing SFT from Magistral Medium traces and RL on top, it's a small, efficient reasoning model with 24B parameters.
Magistral Small can be deployed locally, fitting within a single RTX 4090 or a 32GB RAM MacBook once quantized.
Learn more about Magistral in our blog post.
Key Features
Reasoning: Capable of long chains of reasoning traces before providing an answer.
Multilingual: Supports dozens of languages, including English, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Malay, Nepali, Polish, Portuguese, Romanian, Russian, Serbian, Spanish, Swedish, Turkish, Ukrainian, Vietnamese, Arabic, Bengali, Chinese, and Farsi.
Apache 2.0 License: Open license allowing usage and modification for both commercial and non-commercial purposes.
Context Window: A 128k context window, but performance might degrade past 40k. Hence we recommend setting the maximum model length to 40k.
Repository: localaiLicense: apache-2.0
mistralai_ministral-3-14b-instruct-2512-multimodal
The largest model in the Ministral 3 family, Ministral 3 14B offers frontier capabilities and performance comparable to its larger Mistral Small 3.2 24B counterpart. A powerful and efficient language model with vision capabilities.
The Ministral 3 family is designed for edge deployment, capable of running on a wide range of hardware. Ministral 3 14B can even be deployed locally, capable of fitting in 24GB of VRAM in FP8, and less if further quantized.
Key Features:
Ministral 3 14B consists of two main architectural components:
- 13.5B Language Model
- 0.4B Vision Encoder
The Ministral 3 14B Instruct model offers the following capabilities:
- Vision: Enables the model to analyze images and provide insights based on visual content, in addition to text.
- Multilingual: Supports dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, Arabic.
- System Prompt: Maintains strong adherence and support for system prompts.
- Agentic: Offers best-in-class agentic capabilities with native function calling and JSON outputting.
- Edge-Optimized: Delivers best-in-class performance at a small scale, deployable anywhere.
- Apache 2.0 License: Open-source license allowing usage and modification for both commercial and non-commercial purposes.
- Large Context Window: Supports a 256k context window.
This gallery entry includes mmproj for multimodality and uses Unsloth recommended defaults.
Repository: localaiLicense: apache-2.0
mistralai_ministral-3-14b-reasoning-2512-multimodal
The largest model in the Ministral 3 family, Ministral 3 14B offers frontier capabilities and performance comparable to its larger Mistral Small 3.2 24B counterpart. A powerful and efficient language model with vision capabilities.
This model is the reasoning post-trained version, trained for reasoning tasks, making it ideal for math, coding and stem related use cases.
The Ministral 3 family is designed for edge deployment, capable of running on a wide range of hardware. Ministral 3 14B can even be deployed locally, capable of fitting in 32GB of VRAM in BF16, and less than 24GB of RAM/VRAM when quantized.
Key Features:
Ministral 3 14B consists of two main architectural components:
- 13.5B Language Model
- 0.4B Vision Encoder
The Ministral 3 14B Reasoning model offers the following capabilities:
- Vision: Enables the model to analyze images and provide insights based on visual content, in addition to text.
- Multilingual: Supports dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, Arabic.
- System Prompt: Maintains strong adherence and support for system prompts.
- Agentic: Offers best-in-class agentic capabilities with native function calling and JSON outputting.
- Reasoning: Excels at complex, multi-step reasoning and dynamic problem-solving.
- Edge-Optimized: Delivers best-in-class performance at a small scale, deployable anywhere.
- Apache 2.0 License: Open-source license allowing usage and modification for both commercial and non-commercial purposes.
- Large Context Window: Supports a 256k context window.
This gallery entry includes mmproj for multimodality and uses Unsloth recommended defaults.
Repository: localaiLicense: apache-2.0
mistralai_ministral-3-8b-instruct-2512-multimodal
A balanced model in the Ministral 3 family, Ministral 3 8B is a powerful, efficient tiny language model with vision capabilities.
The Ministral 3 family is designed for edge deployment, capable of running on a wide range of hardware. Ministral 3 8B can even be deployed locally, capable of fitting in 12GB of VRAM in FP8, and less if further quantized.
Key Features:
Ministral 3 8B consists of two main architectural components:
- 8.4B Language Model
- 0.4B Vision Encoder
The Ministral 3 8B Instruct model offers the following capabilities:
- Vision: Enables the model to analyze images and provide insights based on visual content, in addition to text.
- Multilingual: Supports dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, Arabic.
- System Prompt: Maintains strong adherence and support for system prompts.
- Agentic: Offers best-in-class agentic capabilities with native function calling and JSON outputting.
- Edge-Optimized: Delivers best-in-class performance at a small scale, deployable anywhere.
- Apache 2.0 License: Open-source license allowing usage and modification for both commercial and non-commercial purposes.
- Large Context Window: Supports a 256k context window.
This gallery entry includes mmproj for multimodality and uses Unsloth recommended defaults.
Repository: localaiLicense: apache-2.0
mistralai_ministral-3-8b-reasoning-2512-multimodal
A balanced model in the Ministral 3 family, Ministral 3 8B is a powerful, efficient tiny language model with vision capabilities.
This model is the reasoning post-trained version, trained for reasoning tasks, making it ideal for math, coding and stem related use cases.
The Ministral 3 family is designed for edge deployment, capable of running on a wide range of hardware. Ministral 3 8B can even be deployed locally, capable of fitting in 24GB of VRAM in BF16, and less than 12GB of RAM/VRAM when quantized.
Key Features:
Ministral 3 8B consists of two main architectural components:
- 8.4B Language Model
- 0.4B Vision Encoder
The Ministral 3 8B Reasoning model offers the following capabilities:
- Vision: Enables the model to analyze images and provide insights based on visual content, in addition to text.
- Multilingual: Supports dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, Arabic.
- System Prompt: Maintains strong adherence and support for system prompts.
- Agentic: Offers best-in-class agentic capabilities with native function calling and JSON outputting.
- Reasoning: Excels at complex, multi-step reasoning and dynamic problem-solving.
- Edge-Optimized: Delivers best-in-class performance at a small scale, deployable anywhere.
- Apache 2.0 License: Open-source license allowing usage and modification for both commercial and non-commercial purposes.
- Large Context Window: Supports a 256k context window.
This gallery entry includes mmproj for multimodality and uses Unsloth recommended defaults.
Repository: localaiLicense: apache-2.0
mistralai_ministral-3-3b-instruct-2512-multimodal
The smallest model in the Ministral 3 family, Ministral 3 3B is a powerful, efficient tiny language model with vision capabilities.
The Ministral 3 family is designed for edge deployment, capable of running on a wide range of hardware. Ministral 3 3B can even be deployed locally, capable of fitting in 8GB of VRAM in FP8, and less if further quantized.
Key Features:
Ministral 3 3B consists of two main architectural components:
- 3.4B Language Model
- 0.4B Vision Encoder
The Ministral 3 3B Instruct model offers the following capabilities:
- Vision: Enables the model to analyze images and provide insights based on visual content, in addition to text.
- Multilingual: Supports dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, Arabic.
- System Prompt: Maintains strong adherence and support for system prompts.
- Agentic: Offers best-in-class agentic capabilities with native function calling and JSON outputting.
- Edge-Optimized: Delivers best-in-class performance at a small scale, deployable anywhere.
- Apache 2.0 License: Open-source license allowing usage and modification for both commercial and non-commercial purposes.
- Large Context Window: Supports a 256k context window.
This gallery entry includes mmproj for multimodality and uses Unsloth recommended defaults.
Repository: localaiLicense: apache-2.0
mistralai_ministral-3-3b-reasoning-2512-multimodal
The smallest model in the Ministral 3 family, Ministral 3 3B is a powerful, efficient tiny language model with vision capabilities.
This model is the reasoning post-trained version, trained for reasoning tasks, making it ideal for math, coding and stem related use cases.
The Ministral 3 family is designed for edge deployment, capable of running on a wide range of hardware. Ministral 3 3B can even be deployed locally, fitting in 16GB of VRAM in BF16, and less than 8GB of RAM/VRAM when quantized.
Key Features:
Ministral 3 3B consists of two main architectural components:
- 3.4B Language Model
- 0.4B Vision Encoder
The Ministral 3 3B Reasoning model offers the following capabilities:
- Vision: Enables the model to analyze images and provide insights based on visual content, in addition to text.
- Multilingual: Supports dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, Arabic.
- System Prompt: Maintains strong adherence and support for system prompts.
- Agentic: Offers best-in-class agentic capabilities with native function calling and JSON outputting.
- Reasoning: Excels at complex, multi-step reasoning and dynamic problem-solving.
- Edge-Optimized: Delivers best-in-class performance at a small scale, deployable anywhere.
- Apache 2.0 License: Open-source license allowing usage and modification for both commercial and non-commercial purposes.
- Large Context Window: Supports a 256k context window.
This gallery entry includes mmproj for multimodality and uses Unsloth recommended defaults.
Repository: localaiLicense: apache-2.0
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: stabilityai-ai-community
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-dev-ggml
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.
This model is quantized with GGUF
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-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