Modelpublic

zerofata/MS3.2-PaintedFantasy-v4-24B

sourceHugging Facemitupdated 8mo agoView on Hugging Face

20likes162downloads

Model Card

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.container .code-wrapper pre { margin-left: 15px; } </style> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>PaintedFantasy</title> <link rel="preconnect" href="https://fonts.googleapis.com"> <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700;800&family=Source+Sans+Pro:ital,wght@0,400;0,600;1,400&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> </head> <body>

<div class="container"> <div class="ambient-overlay"></div> <div class="corner-accent corner-tl"></div> <div class="corner-accent corner-tr"></div> <div class="corner-accent corner-bl"></div> <div class="corner-accent corner-br"></div> <div class="content-frame"> <div class="title-container"> <div class="ornamental-top"></div> <div class="ornamental-bottom"></div> <div class="title-glow-layer"></div> <div class="title-wrapper"> <div class="title-accent-left"></div> <div class="title-accent-right"></div> <h1 class="title-main"> <span class="title-prefix">Painted Fantasy v4</span> </h1> <div class="version-indicator">Magistral Small 2509 24B</div> </div> </div> <div class="image-frame"> <div class="image-border-accent"></div> <img src="https://cdn-uploads.huggingface.co/production/uploads/65b19c6c638328850e12d38c/D3MkKWLMkJDPW39UohRq.png" alt="image"> </div> <div class="section-container"> <div class="section-frame"> <div class="section-glow"></div> <div class="section-header"> <div class="section-indicator"></div> <h2 class="section-title">Overview</h2> </div> <div class="section-content"> <div class="content-block"> <p></p> <p>This is an uncensored model intended to excel at creative character driven RP / ERP.</p> <p>Feels like a good in between of creativity / dialogue and logic. This version tries to improve on the writing style and intelligence compared to v3.</p> <P>A small portion of reasoning data was included, so thinking using the [THINK][/THINK] tags should still work. Although I haven't tested it personally, as the model is generally intended to be used without reasoning.</p> </div> </div> </div> </div> <div class="section-divider"> <div class="divider-ornament"></div> </div> <div class="section-container"> <div class="section-frame"> <div class="section-glow"></div> <div class="section-header"> <div class="section-indicator"></div> <h2 class="section-title">SillyTavern Settings</h2> </div> <div class="section-content"> <div class="subsection-wrapper"> <h3 class="subheading">Recommended Roleplay Format</h3> <div class="data-box"> <div class="data-box-inner"> <div class="data-row"> <span class="data-arrow">></span> <span class="data-label">Actions:</span> <span class="data-value">In plaintext</span> </div> <div class="data-row"> <span class="data-arrow">></span> <span class="data-label">Dialogue:</span> <span class="data-value">"In quotes"</span> </div> <div class="data-row"> <span class="data-arrow">></span> <span class="data-label">Thoughts:</span> <span class="data-value">*In asterisks*</span> </div> </div> </div> </div> <div class="subsection-wrapper"> <h3 class="subheading">Recommended Samplers</h3> <div class="data-box"> <div class="data-box-inner"> <div class="data-row"> <span class="data-arrow">></span> <span class="data-label">Temp:</span> <span class="data-value">0.8</span> </div> <div class="data-row"> <span class="data-arrow">></span> <span class="data-label">MinP:</span> <span class="data-value">0.05 - 0.075</span> </div> <div class="data-row"> <span class="data-arrow">></span> <span class="data-label">TopP:</span> <span class="data-value">0.95 - 1.00</span> </div> </div> </div> </div> <div class="subsection-wrapper"> <h3 class="subheading">Instruct</h3> <div class="data-box"> <p style="margin: 0;">Mistral v7 Tekken</p> </div> </div> </div> </div> </div> <div class="section-divider"> <div class="divider-ornament"></div> </div> <div class="section-container"> <div class="section-frame"> <div class="section-glow"></div> <div class="section-header"> <div class="section-indicator"></div> <h2 class="section-title">Quantizations</h2> </div> <div class="section-content"> <div class="subsection-wrapper"> <h3 class="subheading">GGUF</h3> <div class="data-box"> <div class="data-box-inner"> <div class="data-row"> <span class="data-arrow">></span> <span class="data-value"><a href="https://huggingface.co/bartowski/zerofataMS3.2-PaintedFantasy-v4-24B-GGUF">iMatrix (bartowski)</a></span> </div> </div> </div> </div> <div class="subsection-wrapper"> <h3 class="subheading">EXL3</h3> <div class="data-box"> <div class="data-box-inner"> <div class="data-row"> <span class="data-arrow">></span> <span class="data-value"><a href="https://huggingface.co/zerofata/MS3.2-PaintedFantasy-v4-24b-exl3-3bpw">3bpw</a></span> </div> <div class="data-row"> <span class="data-arrow">></span> <span class="data-value"><a href="https://huggingface.co/zerofata/MS3.2-PaintedFantasy-v4-24b-exl3-4bpw">4bpw</a></span> </div> <div class="data-row"> <span class="data-arrow">></span> <span class="data-value"><a href="https://huggingface.co/zerofata/MS3.2-PaintedFantasy-v4-24b-exl3-5bpw">5bpw</a></span> </div> <div class="data-row"> <span class="data-arrow">></span> <span class="data-value"><a href="https://huggingface.co/zerofata/MS3.2-PaintedFantasy-v4-24b-exl3-6bpw">6bpw</a></span> </div> </div> </div> </div> </div> </div> </div> <div class="section-divider"> <div class="divider-ornament"></div> </div> <div class="section-container"> <div class="section-frame"> <div class="section-glow"></div> <div class="section-header"> <div class="section-indicator"></div> <h2 class="section-title">Creation Process</h2> </div> <div class="section-content"> <div class="content-block"> <p>Creation Process: SFT > DPO</p> <p>SFT on approx 26 million tokens (18.3 million trainable). Datasets included SFW / NSFW RP, stories, NSFW reddit writing prompts, creative instruct & chat data.</p> <p>90% of the dataset is without thinking, 10% included thinking, using the [THINK][/THINK] tags.</p> <p>All RP data and synthetic stories went through rewriting with GLM 4.7 using hand edited examples as guidelines to improve the response. Rewritten responses were discarded if they failed to reduce the slop score for the message. This reduced the slop by about 25% for each RP / story dataset and made the model noticably more creative with some of its descriptions.</p> <p>Additionally, some extra filtering was run over the datasets, finding about a dozen samples containing uncaught refusals, some messy human data and in general just some conversations that were outliers in low quality that had accumulated since I started building my datasets.</p> <P>DPO was expanded to include non creative datasets. My usual RP DPO dataset (also rewritten) was included along with cybersecurity and two partial subsets of general assistant / chat preference datasets to help stabalize the model. This worked pretty well. While creativity did take a small hit, enough remained that the improved logic resulted in a notably improved model (IMO).</p> </div> <div class="dropdown-container"> <details> <summary class="dropdown-summary"> <div class="summary-inner"> <span class="dropdown-arrow">></span> <span class="summary-text">Axolotl configs</span> </div> </summary> <div class="dropdown-content"> <div class="dropdown-inner"> <p>Not optimized for cost / performance efficiency, YMMV.</p> <div class="config-title">SFT (4*H200)</div> <div class="code-wrapper"> <pre><code>base_model: Darkhn/Magistral-2509-24B-Text-Only

tokenizerusemistral_common: true

plugins: - axolotl.integrations.cutcrossentropy.CutCrossEntropyPlugin

loadin8bit: false loadin4bit: false deepspeed: deepspeed_configs/zero1.json

datasets: - path: ./data/nothinkdataset.jsonl type: chattemplate - path: ./data/thinkdataset.jsonl type: chattemplate datasetpreparedpath: lastrunprepared2 valsetsize: 0.01 output_dir: ./Magi-24B-SFT-v3-10

adapter: lora peftuserslora: true loramodeldir:

sequencelen: 10496 samplepacking: true padtosequence_len: true

lorar: 256 loraalpha: 16 loradropout: 0.05 loratarget_linear: true

wandbproject: Magi-SFT-24B wandbname: Magi-24B-SFT-v3-10

gradientaccumulationsteps: 1 microbatchsize: 4 numepochs: 2 optimizer: adamwbnb8bit lrscheduler: cosine

learningrate: 1.5e-5 weightdecay: 0.01 maxgradnorm: 2.0

bf16: auto tf32: false

gradientcheckpointing: true resumefromcheckpoint: loggingsteps: 1 flash_attention: true

warmupratio: 0.05 evalsperepoch: 3 savesperepoch: 2</code></pre> </div> <br><div class="config-title">DPO (4*H200)</div> <div class="code-wrapper"> <pre><code># ==================== # MODEL CONFIGURATION # ==================== basemodel: ApocalypseParty/Magi-24B-SFT-v3-10 modeltype: MistralForCausalLM tokenizertype: AutoTokenizer chattemplate: mistralv7_tekken

# ==================== # RL/DPO CONFIGURATION # ==================== rl: dpo rl_beta: 0.07

# ==================== # DATASET CONFIGURATION # ==================== datasets: - path: ./data/dpoms32rewrittenhandcrafteddataset.jsonl type: chattemplate.default fieldmessages: messages fieldchosen: chosen fieldrejected: rejected messagepropertymappings: role: role content: content roles: system: ["system"] user: ["user"] assistant: ["assistant"] - path: ./data/dpochubapprovedrewrittendatasetpartial.jsonl type: chattemplate.default fieldmessages: messages fieldchosen: chosen fieldrejected: rejected messagepropertymappings: role: role content: content roles: system: ["system"] user: ["user"] assistant: ["assistant"] - path: ./data/dposecureprogrammingdataset.jsonl type: chattemplate.default fieldmessages: messages fieldchosen: chosen fieldrejected: rejected messagepropertymappings: role: role content: content roles: system: ["system"] user: ["user"] assistant: ["assistant"] - path: ./data/dpowildchatms32chunk1.jsonl type: chattemplate.default fieldmessages: messages fieldchosen: chosen fieldrejected: rejected messagepropertymappings: role: role content: content roles: system: ["system"] user: ["user"] assistant: ["assistant"] - path: ./data/dpoultrafeedbackchunk1.jsonl type: chattemplate.default fieldmessages: messages fieldchosen: chosen fieldrejected: rejected messageproperty_mappings: role: role content: content roles: system: ["system"] user: ["user"] assistant: ["assistant"]

datasetpreparedpath: ./dpodata4 trainon_inputs: false # Only train on assistant responses

# ==================== # QLORA CONFIGURATION # ==================== adapter: lora loadin8bit: false lorar: 128 loraalpha: 16 peftuserslora: true loradropout: 0.1 loratargetlinear: true # loramodulestosave: # Uncomment only if you added NEW tokens

# ==================== # TRAINING PARAMETERS # ==================== numepochs: 1 microbatchsize: 2 gradientaccumulationsteps: 4 learningrate: 2e-6 optimizer: adamwtorchfused lrscheduler: cosine warmupratio: 0.05 weightdecay: 0.01 maxgrad_norm: 1.0

# ==================== # SEQUENCE CONFIGURATION # ==================== sequencelen: 10756 padtosequencelen: true

# ==================== # HARDWARE OPTIMIZATIONS # ==================== bf16: auto tf32: false flashattention: true gradientcheckpointing: offload

plugins: - axolotl.integrations.liger.LigerPlugin - axolotl.integrations.cutcrossentropy.CutCrossEntropyPlugin cutcrossentropy: true ligerrope: true ligerrmsnorm: true ligerlayernorm: true ligergluactivation: true ligercrossentropy: false # Cut Cross Entropy overrides this ligerfusedlinearcrossentropy: false # Cut Cross Entropy overrides this deepspeed: deepspeedconfigs/zero1.json

# ==================== # CHECKPOINTING # ==================== evalsperepoch: 1 savesperepoch: 6 loadbestmodelatend: true metricforbestmodel: evalloss greaterisbetter: false

# ==================== # LOGGING & OUTPUT # ==================== outputdir: ./Magi-24B-SFT-v3-10-DPO-9 loggingsteps: 1 save_safetensors: true

# ==================== # WANDB TRACKING # ==================== wandbproject: Magi-24B-DPO wandbname: Magi-24B-SFT-v3-10-DPO-9</code></pre> </div> </div> </div> </details> </div> </div> </div> </div> </div> </div> </body> </html>