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zerofata/MS3.2-PaintedFantasy-Visage-33B_exl3_3bpw

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face
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<div class="container"> <div class="title-container"> <!-- Glitchy overlay --> <div class="glitchy-overlay"></div> <!-- Main title --> <div class="title-wrapper"> <h1 class="title-main"> PAINTED <span class="lemonade-text">FANTASY</span> VISAGE </h1> <div class="title-subtitle"> <span class="subtitle-text">Mistrall Small 3.2 Upscaled 33B</span> </div> </div> </div>

image/png

<div class="section-container"> <div class="section-header"> <div class="section-indicator"></div> <h2 class="section-title">Overview</h2> </div> <div class="section-content"> <p>Another experimental release. Mistral Small 3.2 24B upscaled by 18 layers to create a 33.6B model. This model then went through pretraining, SFT & DPO.</p> <p>Can't guarantee the Mistral 3.2 repetition issues are fixed, but this model seems to be less repetitive than my previous attempt.</p> <p>This is an uncensored creative model intended to excel at character driven RP / ERP where characters are portrayed creatively and proactively.</p> </div> </div>

<div class="section-container"> <div class="section-header"> <div class="section-indicator"></div> <h2 class="section-title">SillyTavern Settings</h2> </div> <div class="section-content"> <h3 class="subheading">Recommended Roleplay Format</h3> <div class="data-box"> <div class="data-row"> <span class="data-arrow">></span> <span class="data-label">Actions:</span> <span>In plaintext</span> </div> <div class="data-row"> <span class="data-arrow">></span> <span class="data-label">Dialogue:</span> <span>"In quotes"</span> </div> <div class="data-row"> <span class="data-arrow">></span> <span class="data-label">Thoughts:</span> <span>In asterisks</span> </div> </div> <h3 class="subheading">Recommended Samplers</h3> <div class="data-box"> <div class="data-row"> <span class="data-arrow">></span> <span class="data-label">Temp:</span> <span>0.6</span> </div> <div class="data-row"> <span class="data-arrow">></span> <span class="data-label">MinP:</span> <span>0.03 - 0.05</span> </div> <div class="data-row"> <span class="data-arrow">></span> <span class="data-label">TopP:</span> <span>0.95 - 1.0</span> </div> <div class="data-row"> <span class="data-arrow">></span> <span class="data-label">Dry:</span> <span>0.8, 1.75, 4</span> </div> </div> <h3 class="subheading">Instruct</h3> <div class="data-box"> <p style="margin: 0;">Mistral v7 Tekken</p> </div> </div> </div>

<div class="section-container"> <div class="section-header"> <div class="section-indicator"></div> <h2 class="section-title">Quantizations</h2> </div> <div class="section-content"> <div style="margin-bottom: 20px;"> <h3 class="subheading">GGUF</h3> <div class="data-box"> <div class="data-row"> <a href="https://huggingface.co/mradermacher/MS3.2-PaintedFantasy-Visage-33B-GGUF">Static (mradermacher)</a> </div> <div class="data-row"> <a href="https://huggingface.co/mradermacher/MS3.2-PaintedFantasy-Visage-33B-i1-GGUF">iMatrix (mradermacher)</a> </div> </div> </div> <div> <h3 class="subheading">EXL3</h3> <div class="data-box"> <div class="data-row"> <a href="https://huggingface.co/zerofata/MS3.2-PaintedFantasy-Visage-33Bexl33bpw">3bpw</a> </div> <div class="data-row"> <a href="https://huggingface.co/zerofata/MS3.2-PaintedFantasy-Visage-33Bexl34bpw">4bpw</a> </div> <div class="data-row"> <a href="https://huggingface.co/zerofata/MS3.2-PaintedFantasy-Visage-33Bexl35bpw">5bpw</a> </div> <div class="data-row"> <a href="https://huggingface.co/zerofata/MS3.2-PaintedFantasy-Visage-33Bexl36bpw">6bpw</a> </div> </div> </div> </div> </div>

<div class="section-container"> <div class="section-header"> <div class="section-indicator"></div> <h2 class="section-title">Creation Process</h2> </div> <div class="section-content"> <p>Creation process: Upscale > Pretrain > SFT > DPO</p> <p>All training was qlora (including pretrain).</p> <p>Pretrained on 177MB of data. Dataset consisteted mostly of Light Novels, NSFW stories, SFW stories and filled out with general corpus text from Huggingface FineWeb-2 dataset.</p> <p>The model then went through SFT using a dataset of approx 3.6 million tokens, 700 RP conversations, 1000 creative writing / instruct samples and about 100 summaries. The bulk of this data has been made public.</p> <p>Finally, DPO was used to make the model more consistent.</p> <div class="dropdown-container"> <details> <summary class="dropdown-summary"> <span class="dropdown-arrow">></span> Mergekit Config </summary> <div class="dropdown-content"> <pre><code>basemodel: anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-Text-Only mergemethod: passthrough dtype: bfloat16 slices:

  • —sources:
  • —model: anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-Text-Only layer_range: [0, 29]
  • —sources:
  • —model: anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-Text-Only layerrange: [10, 39] </code></pre> </div> </details> </div> <div class="dropdown-container"> <details> <summary class="dropdown-summary"> <span class="dropdown-arrow">></span> Axolotl configs </summary> <div class="dropdown-content"> <p>Not optimized for cost / performance efficiency, YMMV.</p> <div class="config-title">SFT 1*H100</div> <pre><code>&#35; ==================== &#35; MODEL CONFIGURATION &#35; ==================== basemodel: ./UpscaleMistral-PT/merged modeltype: AutoModelForCausalLM tokenizertype: AutoTokenizer chattemplate: mistralv7tekken <br> &#35; ==================== &#35; DATASET CONFIGURATION &#35; ==================== datasets:
  • —path: ./dataset.jsonl type: chattemplate split: train chattemplatestrategy: tokenizer fieldmessages: messages messagepropertymappings: role: role content: content roles: user: ["user"] assistant: ["assistant"] system: ["system"]

datasetpreparedpath: trainoninputs: false &#35; Only train on assistant responses

&#35; ==================== &#35; QLORA CONFIGURATION &#35; ==================== adapter: qlora loadin4bit: true lorar: 128 loraalpha: 128 loradropout: 0.1 loratargetlinear: true &#35; loramodulestosave: &#35; Uncomment only if you added NEW tokens

&#35; ==================== &#35; TRAINING PARAMETERS &#35; ==================== numepochs: 2 microbatchsize: 4 gradientaccumulationsteps: 2 learningrate: 1.5e-5 optimizer: pagedadamw8bit lrscheduler: rex warmupratio: 0.05 weightdecay: 0.01 maxgrad_norm: 1.0

&#35; ==================== &#35; SEQUENCE &amp; PACKING &#35; ==================== sequencelen: 8192 samplepacking: true evalsamplepacking: false padtosequence_len: true

&#35; ==================== &#35; HARDWARE OPTIMIZATIONS &#35; ==================== bf16: auto flashattention: true gradientcheckpointing: true

&#35; ==================== &#35; EVALUATION &amp; CHECKPOINTING &#35; ==================== savestrategy: steps savesteps: 5 savetotallimit: 5 &#35; Keep best + last few checkpoints loadbestmodelatend: true greaterisbetter: false

&#35; ==================== &#35; LOGGING &amp; OUTPUT &#35; ==================== outputdir: ./UpscaleMistral-PT-SFT-2 loggingsteps: 2 savesafetensors: true

&#35; ==================== &#35; WANDB TRACKING &#35; ==================== wandbproject: MS3-2-SFT wandbentity: yourentity wandbname: run_name<p></p></code></pre> </div> </details> </div> </div> </div> </div> </body> </html>