SD-prompter-4B-V0.2-exl2

exl2 quant (measurement.json in main branch)

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check revisions for quants

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<!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Model README</title> <style> body { background: linear-gradient(-45deg, #0a0a0a, #121212, #1a1a1a); color: #E0E0E0; font-family: 'Segoe UI', system-ui; margin: 0; padding: 20px; min-height: 100vh; animation: gradient 15s ease infinite; background-size: 400% 400%; text-align: center; } @keyframes gradient { 0% { background-position: 0% 50%; } 50% { background-position: 100% 50%; } 100% { background-position: 0% 50%; } } .container { max-width: 800px; margin: auto; } .model-image { width: 100%; border-radius: 12px; filter: drop-shadow(0 0 10px rgba(255, 255, 255, 0.1)); animation: float 6s ease-in-out infinite; } @keyframes float { 0%, 100% { transform: translateY(0); } 50% { transform: translateY(-20px); } } .box { background: rgba(30, 30, 30, 0.9); border-radius: 12px; padding: 20px; margin: 25px 0; backdrop-filter: blur(10px); border: 1px solid rgba(255, 255, 255, 0.1); text-align: left; } h2 { border-left: 4px solid #0ff; padding-left: 15px; margin: 0 0 15px 0; background: linear-gradient(90deg, transparent, rgba(0, 255, 255, 0.1)); text-transform: uppercase; letter-spacing: 2px; color: #fff; } .yaml-content { background: #191919; border-radius: 8px; padding: 10px; margin-top: 10px; font-family: monospace; white-space: pre-wrap; color: #E0E0E0; border-left: 4px solid #0ff; } /* Custom Scrollbar */ ::-webkit-scrollbar { width: 8px; } ::-webkit-scrollbar-track { background: #121212; } ::-webkit-scrollbar-thumb { background: #333; border-radius: 4px; } </style> </head> <body> <div class="container"> <img src="https://huggingface.co/Delta-Vector/SD-prompter-4B-V0.2/resolve/main/egrgre-removebg-preview.png" class="model-image" alt="Model Visualization"> <div class="box"> <h2>🔍 Overview</h2> <p>This is the second in a line of models dedicated to creating Stable-Diffusion prompts when given a character appearance. Made for the CharGen Project, This has been finetuned ontop of Delta-Vector/Holland-4B-V1</> </div> <div class="box"> <h2>⚖️ Quants</h2> <p>Available quantization formats:</p> <ul> <li>GGUF: https://huggingface.co/mradermacher/SDPrompter4b-GGUF</li> <li>EXL2: https://huggingface.co/Delta-Vector/SD-prompter-4B-V0.2-exl2/</li> </ul> </div> <div class="box"> <h2>💬 Prompting</h2> <p><strong>Recommended format: ChatML, Use the following system prompt for the model. I'd advise against setting a high amount of output tokens as the model loops, use 0.1 min-p and temp-1 to keep it coherent.</strong></p> <code>Create a prompt for Stable Diffusion based on the information below.</code> </div> <div class="box"> <h2>🌟 Credits</h2> <p>Finetuned on 1xRTX6000 provided by Kubernetes_bad, All credits goes to Kubernetes_bad, LucyKnada and the rest of Anthracite.</p> </div> <div class="box"> <h2>🛠️ Axolotl Config)</h2> <pre> base_model: Delta-Vector/Holland-4B-V1 model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer trust_remote_code: true load_in_8bit: false load_in_4bit: false strict: false datasets: - path: NewEden/CivitAI-SD-Prompts datasets: - path: NewEden/CivitAI-Prompts-Sharegpt type: chat_template chat_template: chatml roles_to_train: ["gpt"] field_messages: conversations message_field_role: from message_field_content: value train_on_eos: turn dataset_prepared_path: val_set_size: 0.02 output_dir: ./outputs/out2 sequence_len: 8192 sample_packing: true eval_sample_packing: false pad_to_sequence_len: true plugins: - axolotl.integrations.liger.LigerPlugin liger_rope: true liger_rms_norm: true liger_swiglu: true liger_fused_linear_cross_entropy: true wandb_project: SDprompter-final wandb_entity: wandb_watch: wandb_name: SDprompter-final wandb_log_model: gradient_accumulation_steps: 16 micro_batch_size: 1 num_epochs: 4 optimizer: paged_adamw_8bit lr_scheduler: cosine learning_rate: 0.00001 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: true gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_ratio: 0.05 evals_per_epoch: 4 saves_per_epoch: 1 debug: weight_decay: 0.01 special_tokens: pad_token: <|finetune_right_pad_id|> eos_token: <|eot_id|> auto_resume_from_checkpoints: true </pre> </div> </div> </div> </body> </html>