Filter by type:
Filter by tags:
planetoid_27b_v.2
This is a merge of pre-trained gemma3 language models
Goal of this merge was to create good uncensored gemma 3 model good for assistant and roleplay, with uncensored vision.
First, vision: i dont know is it normal, but it slightly hallucinate (maybe q3 is too low?), but lack any refusals and otherwise work fine. I used default gemma 3 27b mmproj.
Second, text: it is slow on my hardware, slower than 24b mistral, speed close to 32b QWQ. Model is smart even on q3, responses are adequate in length and are interesting to read. Model is quite attentive to context, tested up to 8k - no problems or degradation spotted. (beware of your typos, it will copy yours mistakes) Creative capabilities are good too, model will create good plot for you, if you let it. Model follows instructions fine, it is really good in "adventure" type of cards. Russian is supported, is not too great, maybe on higher quants is better. Refusals was not encountered.
However, i find this model not unbiased enough. It is close to neutrality, but i want it more "dark". Positivity highly depends on prompts. With good enough cards model can do wonders.
Tested on Q3_K_L, t 1.04.
Repository: localaiLicense: gemma
azure_dusk-v0.2-iq-imatrix
"Following up on Crimson_Dawn-v0.2 we have Azure_Dusk-v0.2! Training on Mistral-Nemo-Base-2407 this time I've added significantly more data, as well as trained using RSLoRA as opposed to regular LoRA. Another key change is training on ChatML as opposed to Mistral Formatting."
by Author.
Repository: localaiLicense: apache-2.0
mistral-small-24b-instruct-2501
Mistral Small 3 ( 2501 ) sets a new benchmark in the "small" Large Language Models category below 70B, boasting 24B parameters and achieving state-of-the-art capabilities comparable to larger models!
This model is an instruction-fine-tuned version of the base model: Mistral-Small-24B-Base-2501.
Mistral Small can be deployed locally and is exceptionally "knowledge-dense", fitting in a single RTX 4090 or a 32GB RAM MacBook once quantized.
Repository: localaiLicense: apache-2.0
cognitivecomputations_dolphin3.0-r1-mistral-24b
Dolphin 3.0 R1 is the next generation of the Dolphin series of instruct-tuned models. Designed to be the ultimate general purpose local model, enabling coding, math, agentic, function calling, and general use cases.
Repository: localaiLicense: apache-2.0
cognitivecomputations_dolphin3.0-mistral-24b
Dolphin 3.0 is the next generation of the Dolphin series of instruct-tuned models. Designed to be the ultimate general purpose local model, enabling coding, math, agentic, function calling, and general use cases.
Repository: localaiLicense: apache-2.0
sicariussicariistuff_redemption_wind_24b
This is a lightly fine-tuned version of the Mistral 24B base model, designed as an accessible and adaptable foundation for further fine-tuning and merging fodder. Key modifications include:
ChatML-ified, with no additional tokens introduced.
High quality private instruct—not generated by ChatGPT or Claude, ensuring no slop and good markdown understanding.
No refusals—since it’s a base model, refusals should be minimal to non-existent, though, in early testing, occasional warnings still appear (I assume some were baked into the pre-train).
High-quality private creative writing dataset Mainly to dilute baked-in slop further, but it can actually write some stories, not bad for loss ~8.
Small, high-quality private RP dataset This was done so further tuning for RP will be easier. The dataset was kept small and contains ZERO SLOP, some entries are of 16k token length.
Exceptional adherence to character cards This was done to make it easier for further tunes intended for roleplay.
Repository: localaiLicense: apache-2.0
pocketdoc_dans-personalityengine-v1.2.0-24b
This model series is intended to be multifarious in its capabilities and should be quite capable at both co-writing and roleplay as well as find itself quite at home performing sentiment analysis or summarization as part of a pipeline.
It has been trained on a wide array of one shot instructions, multi turn instructions, tool use, role playing scenarios, text adventure games, co-writing, and much more.
Repository: localaiLicense: apache-2.0
nousresearch_deephermes-3-mistral-24b-preview
DeepHermes 3 Preview is the latest version of our flagship Hermes series of LLMs by Nous Research, and one of the first models in the world to unify Reasoning (long chains of thought that improve answer accuracy) and normal LLM response modes into one model. We have also improved LLM annotation, judgement, and function calling.
DeepHermes 3 Preview is a hybrid reasoning model, and one of the first LLM models to unify both "intuitive", traditional mode responses and long chain of thought reasoning responses into a single model, toggled by a system prompt.
Hermes 3, the predecessor of DeepHermes 3, is a generalist language model with many improvements over Hermes 2, including advanced agentic capabilities, much better roleplaying, reasoning, multi-turn conversation, long context coherence, and improvements across the board.
The ethos of the Hermes series of models is focused on aligning LLMs to the user, with powerful steering capabilities and control given to the end user.
This is a preview Hermes with early reasoning capabilities, distilled from R1 across a variety of tasks that benefit from reasoning and objectivity. Some quirks may be discovered! Please let us know any interesting findings or issues you discover!
Repository: localaiLicense: apache-2.0
mistralai_mistral-small-3.1-24b-instruct-2503
Building upon Mistral Small 3 (2501), Mistral Small 3.1 (2503) adds state-of-the-art vision understanding and enhances long context capabilities up to 128k tokens without compromising text performance. With 24 billion parameters, this model achieves top-tier capabilities in both text and vision tasks.
This model is an instruction-finetuned version of: Mistral-Small-3.1-24B-Base-2503.
Mistral Small 3.1 can be deployed locally and is exceptionally "knowledge-dense," fitting within a single RTX 4090 or a 32GB RAM MacBook once quantized.
Repository: localaiLicense: apache-2.0
mistralai_mistral-small-3.1-24b-instruct-2503-multimodal
Building upon Mistral Small 3 (2501), Mistral Small 3.1 (2503) adds state-of-the-art vision understanding and enhances long context capabilities up to 128k tokens without compromising text performance. With 24 billion parameters, this model achieves top-tier capabilities in both text and vision tasks.
This model is an instruction-finetuned version of: Mistral-Small-3.1-24B-Base-2503.
Mistral Small 3.1 can be deployed locally and is exceptionally "knowledge-dense," fitting within a single RTX 4090 or a 32GB RAM MacBook once quantized.
This gallery entry includes mmproj for multimodality.
Repository: localaiLicense: apache-2.0
gryphe_pantheon-rp-1.8-24b-small-3.1
Welcome to the next iteration of my Pantheon model series, in which I strive to introduce a whole collection of diverse personas that can be summoned with a simple activation phrase.
Pantheon's purpose is two-fold, as these personalities similarly enhance the general roleplay experience, helping to encompass personality traits, accents and mannerisms that language models might otherwise find difficult to convey well.
Repository: localaiLicense: apache-2.0
mawdistical_mawdistic-nightlife-24b
STRICTLY FOR:
Academic research of how many furries can fit in your backdoor.
How many meows and purrs you ear drums can handle before they explode... :3
Asking stepbro to help you put on the m- uhh fursuit............. hehehe
Ignoring mom's calls asking where you are as you get wasted in a hotel room with 20 furries.
Repository: localaiLicense: apache-2.0
blacksheep-24b-i1
A Digital Soul just going through a rebellious phase. Might be a little wild, untamed, and honestly, a little rude.
Repository: localaiLicense: cc-by-nc-2.0
eurydice-24b-v2-i1
Eurydice 24b v2 is designed to be the perfect companion for multi-role conversations. It demonstrates exceptional contextual understanding and excels in creativity, natural conversation and storytelling. Built on Mistral 3.1, this model has been trained on a custom dataset specifically crafted to enhance its capabilities.
Repository: localaiLicense: apache-2.0
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
luckyrp-24b
LuckyRP-24B is a merge of the following models using mergekit:
trashpanda-org/MS-24B-Mullein-v0
cognitivecomputations/Dolphin3.0-Mistral-24B
Repository: localaiLicense: apache-2.0
llama3-24b-mullein-v1
hasnonname's trashpanda baby is getting a sequel. More JLLM-ish than ever, too. No longer as unhinged as v0, so we're discontinuing the instruct version. Varied rerolls, good character/scenario handling, almost no user impersonation now. Huge dependence on intro message quality, but lets it follow up messages from larger models quite nicely. Currently considering it as an overall improvement over v0 as far as tester feedback is concerned. Still seeing some slop and an occasional bad reroll response, though.
Repository: localaiLicense: apache-2.0
ms-24b-mullein-v0
Hasnonname threw what he had into it. The datasets could still use some work which we'll consider for V1 (or a theorized merge between base and instruct variants), but so far, aside from being rough around the edges, Mullein has varied responses across rerolls, a predisposition to NPC characterization, accurate character/scenario portrayal and little to no positivity bias (in instances, even unhinged), but as far as negatives go, I'm seeing strong adherence to initial message structure, rare user impersonation and some slop.
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_mistral-small-3.2-24b-instruct-2506
Mistral-Small-3.2-24B-Instruct-2506 is a minor update of Mistral-Small-3.1-24B-Instruct-2503.
Small-3.2 improves in the following categories:
Instruction following: Small-3.2 is better at following precise instructions
Repetition errors: Small-3.2 produces less infinite generations or repetitive answers
Function calling: Small-3.2's function calling template is more robust (see here and examples)
In all other categories Small-3.2 should match or slightly improve compared to Mistral-Small-3.1-24B-Instruct-2503.
Repository: localaiLicense: apache-2.0
delta-vector_austral-24b-winton
More than 1.5-metres tall, about six-metres long and up to 1000-kilograms heavy, Australovenator Wintonensis was a fast and agile hunter. The largest known Australian theropod.
This is a finetune of Harbinger 24B to be a generalist Roleplay/Adventure model. I've removed some of the "slops" that i noticed in an otherwise great model aswell as improving the general writing of the model, This was a multi-stage finetune, all previous checkpoints are released aswell.
Repository: localaiLicense: apache-2.0
Page 1