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SDPrompter4b-ckpts

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Delta-Vector commited on Feb 17, 2025

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@@ -1,34 +1,111 @@
----
-library_name: transformers
-license: agpl-3.0
-base_model: Delta-Vector/Holland-4B-V1
-tags:
-- generated_from_trainer
-datasets:
-- NewEden/CivitAI-Prompts-Sharegpt
-model-index:
-- name: outputs/out2
-  results: []
----
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
-<details><summary>See axolotl config</summary>
-axolotl version: `0.6.0`
-```yaml
 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:
@@ -40,7 +117,6 @@ datasets:
     message_field_role: from
     message_field_content: value
     train_on_eos: turn
 dataset_prepared_path:
 val_set_size: 0.02
 output_dir: ./outputs/out2
@@ -48,33 +124,28 @@ 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
@@ -84,83 +155,18 @@ 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
-```
-</details><br>
-# outputs/out2
-This model is a fine-tuned version of [Delta-Vector/Holland-4B-V1](https://huggingface.co/Delta-Vector/Holland-4B-V1) on the NewEden/CivitAI-Prompts-Sharegpt dataset.
-It achieves the following results on the evaluation set:
-- Loss: 3.2782
-## Model description
-More information needed
-## Intended uses & limitations
-More information needed
-## Training and evaluation data
-More information needed
-## Training procedure
-### Training hyperparameters
-The following hyperparameters were used during training:
-- learning_rate: 1e-05
-- train_batch_size: 1
-- eval_batch_size: 1
-- seed: 42
-- gradient_accumulation_steps: 16
-- total_train_batch_size: 16
-- optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_steps: 4
-- num_epochs: 4
-### Training results
-| Training Loss | Epoch  | Step | Validation Loss |
-|:-------------:|:------:|:----:|:---------------:|
-| 3.3357        | 0.0416 | 1    | 4.2492          |
-| 2.9892        | 0.2494 | 6    | 3.6285          |
-| 2.7364        | 0.4987 | 12   | 3.4675          |
-| 2.7076        | 0.7481 | 18   | 3.3928          |
-| 2.757         | 0.9974 | 24   | 3.3484          |
-| 2.5801        | 1.2078 | 30   | 3.3286          |
-| 2.6156        | 1.4571 | 36   | 3.3111          |
-| 2.5308        | 1.7065 | 42   | 3.2999          |
-| 2.5481        | 1.9558 | 48   | 3.2880          |
-| 2.5773        | 2.1662 | 54   | 3.2840          |
-| 2.5269        | 2.4156 | 60   | 3.2822          |
-| 2.5418        | 2.6649 | 66   | 3.2806          |
-| 2.4584        | 2.9143 | 72   | 3.2791          |
-| 2.6515        | 3.1247 | 78   | 3.2789          |
-| 2.4883        | 3.3740 | 84   | 3.2785          |
-| 2.4193        | 3.6234 | 90   | 3.2787          |
-| 2.4337        | 3.8727 | 96   | 3.2782          |
-### Framework versions
-- Transformers 4.47.1
-- Pytorch 2.5.1+cu124
-- Datasets 3.2.0
-- Tokenizers 0.21.0

+<!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="your-image-url" 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/</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:
     message_field_role: from
     message_field_content: value
     train_on_eos: turn
 dataset_prepared_path:
 val_set_size: 0.02
 output_dir: ./outputs/out2
 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
 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>