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MS3.2-24b-Angel

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metadata
base_model:
  - mistralai/Mistral-Small-3.2-24B-Instruct-2506
library_name: transformers
tags:
  - axolotl
  - unsloth
  - roleplay
  - conversational
datasets:
  - PygmalionAI/PIPPA
  - Alfitaria/nemotron-ultra-reasoning-synthkink
  - PocketDoc/Dans-Prosemaxx-Gutenberg
  - FreedomIntelligence/Medical-R1-Distill-Data
  - cognitivecomputations/SystemChat-2.0
  - allenai/tulu-3-sft-personas-instruction-following
  - kalomaze/Opus_Instruct_25k
  - simplescaling/s1K-claude-3-7-sonnet
  - ai2-adapt-dev/flan_v2_converted
  - grimulkan/theory-of-mind
  - grimulkan/physical-reasoning
  - nvidia/HelpSteer3
  - nbeerbower/gutenberg2-dpo
  - nbeerbower/gutenberg-moderne-dpo
  - nbeerbower/Purpura-DPO
  - antiven0m/physical-reasoning-dpo
  - allenai/tulu-3-IF-augmented-on-policy-70b
  - allenai/href

Angel 24b

Very cursed generation of Angel Dust laying on a bed, staring at the camera smirking. Generated with Kiwimix-XL v3 + an Angel Dust lora, refined with GPT-image-1

Better to reign in Hell than serve in Heaven.

Overview

MS3.2-24b-Angel is a model finetuned from Mistral Small 3.2 for roleplaying, storywriting, and differently-flavored general instruct usecases.

Testing revealed strong prose and character portrayal for its class, rivalling the preferred 72B models of some testers.

Quantizations

EXL3:

GGUF:

MLX:

Usage

  • Use Mistral v7 Tekken.
  • It is highly recommended (if your framework supports it) to use the official Mistral tokenization code instead of Huggingface's. This is possible in vLLM with --tokenizer-mode mistral.
  • Recommended samplers (from CURSE and corroborated by me, Fizz) are 1.2 temperature, 0.1 min_p, and 1.05 repetition penalty.
  • We recommend a system prompt, but its contents only faintly matter (I accidentally had an assistant system prompt during the entire time I was testing)

Training Process

  1. The original model had its vision adapter removed for better optimization and easier usage in training frameworks
  2. The model was then put through an SFT process (using Axolotl) on various sources of general instruct, storytelling, and RP data, which resulted in allura-forge/ms32-sft-merged.
  3. Afterwards, the model was put through a KTO process (using Unsloth) on more focused storywriting and anti-slop data, as well as general instruction following and human preference, which resulted in the final checkpoints at allura-forge/ms32-final-TEXTONLY.
  4. Finally, the vision tower was manually added back to the weights to continue to support multimodality.

Credits

  • Fizz - training and data wrangling
  • Artus (by proxy) & Bot - help with funding
  • CURSE - testing
  • Mango - testing, data, help with KTO configs
  • DoctorShotgun - making the original text-only model
  • Axolotl & Unsloth - creating the training frameworks used for parts of this finetune
  • Everyone in Allura - moral support, being cool
  • Vivziepop and co - Angel Dust

<3 love you all