Update README.md

README.md

@@ -1,7 +1,19 @@
 ---
 library_name: transformers
 license: other
+license_name: qwen
+license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
 base_model: Qwen/Qwen2.5-72B
+datasets:
+- anthracite-org/kalo-opus-instruct-22k-no-refusal
+- Nopm/Opus_WritingStruct
+- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
+- Gryphe/Sonnet3.5-Charcard-Roleplay
+- Gryphe/ChatGPT-4o-Writing-Prompts
+- Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
+- Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
+- nothingiisreal/Reddit-Dirty-And-WritingPrompts
+- allura-org/Celeste-1.x-data-mixture
 tags:
 - generated_from_trainer
 model-index:
@@ -9,10 +21,55 @@ model-index:
   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)
+# EVA Qwen2.5-72B v0.0
+
+<p>
+  A RP/storywriting specialist model, full-parameter finetune of Qwen2.5-72B on mixture of synthetic and natural data.<br>
+  It uses Celeste 70B 0.1 data mixture, greatly expanding it to improve versatility, creativity and "flavor" of the resulting model.<br>
+</p>
+
+<p>Note: using quantized KV cache with Qwen2.5 <b>is not recommended</b> and can lead to degraded output quality. On the other hand, Qwen's KV cache is already light enough, so using f16 for it shouldn't be problematic.</p>
+
+<p>
+  <p>Prompt format is ChatML.</p><br>
+  <h3>Recommended sampler values:</h3>
+  <ul>
+  <li>Temperature: 1</li>
+  <li>Typical-P: 0.9</li>
+  <li>Min-P: 0.05</li>
+  <li>Top-A: 0.2</li>
+  <li>Repetition Penalty: 1.03</li>
+  </ul>
+  
+  <h3>Recommended SillyTavern presets (via CalamitousFelicitousness):</h3>
+
+  - [Context](https://huggingface.co/EVA-UNIT-01/EVA-Yi-1.5-9B-32K-V1/blob/main/%5BChatML%5D%20Roleplay-v1.9%20Context.json)
+  - [Instruct and System Prompt](https://huggingface.co/EVA-UNIT-01/EVA-Yi-1.5-9B-32K-V1/blob/main/%5BChatML%5D%20Roleplay-v1.9%20Instruct.json)
+</p>
+
+<p>
+  <br>
+  <h3>
+    Training data:
+  </h3>
+    <ul>
+      <li>Celeste 70B 0.1 data mixture minus Opus Instruct subset. See that model's <a href=https://huggingface.co/nothingiisreal/L3.1-70B-Celeste-V0.1-BF16>card</a> for details.</li>
+      <li>Kalomaze's Opus_Instruct_25k dataset, filtered for refusals.</li>
+      <li>A subset (1k rows) of ChatGPT-4o-WritingPrompts by Gryphe</li>
+      <li>A subset (2k rows) of Sonnet3.5-Charcards-Roleplay by Gryphe</li>
+      <li>Synthstruct and SynthRP datasets by Epiculous</li>
+    </ul>
+  <h3>
+     Training time and hardware:
+  </h3>
+      <ul><li>12 hours on 8xMI300X</li></ul><br>
+</p>
+  <p>Model was trained by Kearm and Auri.</p>
+  <h4>Special thanks:</h4><ul>
+  <li>to Gryphe, Lemmy, Kalomaze, Nopm and Epiculous for the data</li>
+  <li>to CalamitiousFelicitousness for providing free inference for public beta testing</li>
+  <li>and to Allura-org for support and feedback on EVA models.</li></ul>
+<a href=https://github.com/axolotl-ai-cloud/axolotl><img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/></a>
 <details><summary>See axolotl config</summary>
 
 axolotl version: `0.4.1`
@@ -422,66 +479,4 @@ weight_decay: 0.1
 #   fsdp_mixed_precision: BF16  # Added
 ```
 
-</details><br>
-
-# EVA-Qwen2.5-72B-SFFT-v0.0
-
-This model is a fine-tuned version of [Qwen/Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 3.2818
-
-## 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: 5e-05
-- train_batch_size: 4
-- eval_batch_size: 4
-- seed: 42
-- distributed_type: multi-GPU
-- num_devices: 8
-- gradient_accumulation_steps: 4
-- total_train_batch_size: 128
-- total_eval_batch_size: 32
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_steps: 20
-- num_epochs: 3
-
-### Training results
-
-| Training Loss | Epoch  | Step | Validation Loss |
-|:-------------:|:------:|:----:|:---------------:|
-| 1.3286        | 0.0142 | 1    | 2.9734          |
-| 1.0713        | 0.2562 | 18   | 3.7951          |
-| 0.9051        | 0.5125 | 36   | 3.3342          |
-| 0.8746        | 0.7687 | 54   | 3.2625          |
-| 0.6216        | 1.0214 | 72   | 3.2244          |
-| 0.6158        | 1.2786 | 90   | 3.2810          |
-| 0.57          | 1.5357 | 108  | 3.2375          |
-| 0.5213        | 1.7929 | 126  | 3.1606          |
-| 0.3178        | 2.0427 | 144  | 3.2384          |
-| 0.2809        | 2.2989 | 162  | 3.2971          |
-| 0.3067        | 2.5552 | 180  | 3.2886          |
-| 0.3005        | 2.8114 | 198  | 3.2818          |
-
-
-### Framework versions
-
-- Transformers 4.45.2
-- Pytorch 2.5.0+rocm6.1
-- Datasets 3.0.1
-- Tokenizers 0.20.1
+</details><br>