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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
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
alamios_mistral-small-3.1-draft-0.5b
This model is meant to be used as draft model for speculative decoding with mistralai/Mistral-Small-3.1-24B-Instruct-2503 or mistralai/Mistral-Small-24B-Instruct-2501
Data info
The data are Mistral's outputs and includes all kind of tasks from various datasets in English, French, German, Spanish, Italian and Portuguese. It has been trained for 2 epochs on 20k unique examples, for a total of 12 million tokens per epoch.
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
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