Update README.md

README.md

@@ -2,6 +2,17 @@
 library_name: transformers
 license: apache-2.0
 base_model: Qwen/Qwen2.5-14B
+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
+- cognitivecomputations/dolphin-2.9.3
 tags:
 - generated_from_trainer
 model-index:
@@ -9,8 +20,57 @@ 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. -->
+
+# EVA Qwen2.5-14B v0.2
+
+<p>
+  A RP/storywriting specialist model, full-parameter finetune of Qwen2.5-14B 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><b>Version notes for 0.2</b>: Now using the refined dataset from 32B 0.2. Major improvements in coherence, instruction following and long-context comprehension over 14B v0.1.</p>
+
+<p>
+  <p>Prompt format is ChatML.</p><br>
+  <h3>Recommended sampler values:</h3>
+  <ul>
+  <li>Temperature: 0.8</li>
+  <li>Min-P: 0.05</li>
+  <li>Top-A: 0.3</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>
+      <li>A subset from Dolphin-2.9.3, including filtered version of not_samantha and a small subset of systemchat.</li>
+    </ul>
+  <h3>
+     Training time and hardware:
+  </h3>
+      <ul><li> 3 hours on 8xH100 SXM, provided by <a href=https://featherless.ai/>FeatherlessAI</a></li></ul><br>
+</p>
+  <p>Model was created by Kearm, Auri and Cahvay.</p>
+  <h4>Special thanks:</h4><ul>
+  <li><b>to Cahvay for his work on investigating and reprocessing the corrupted dataset, removing the single biggest source of data poisoning.</b></li>
+  <li><b>to <a href=https://featherless.ai/>FeatherlessAI</a> for generously providing 8xH100 SXM node for training of this model</b></li>
+  <li>to Gryphe, Lemmy, Kalomaze, Nopm, Epiculous and CogninitiveComputations for the data</li>
+  <li>and to Allura-org for support, feedback, beta-testing and doing quality control of EVA models.</li></ul>
+
 
 [<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>
@@ -359,66 +419,4 @@ weight_decay: 0.1
 #   fsdp_mixed_precision: BF16  # Added
 ```
 
-</details><br>
-
-# EVA-Qwen2.5-14B-SFFT-v0.2
-
-This model is a fine-tuned version of [Qwen/Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 3.0986
-
-## 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: 2
-- eval_batch_size: 2
-- seed: 42
-- distributed_type: multi-GPU
-- num_devices: 8
-- gradient_accumulation_steps: 8
-- total_train_batch_size: 128
-- total_eval_batch_size: 16
-- 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 |
-|:-------------:|:------:|:----:|:---------------:|
-| 1.4236        | 0.0170 | 1    | 2.6557          |
-| 1.2513        | 0.2553 | 15   | 3.4606          |
-| 1.1338        | 0.5106 | 30   | 3.5536          |
-| 1.0985        | 0.7660 | 45   | 3.1957          |
-| 0.8794        | 1.0170 | 60   | 3.0346          |
-| 0.8584        | 1.2718 | 75   | 3.0551          |
-| 0.8421        | 1.5265 | 90   | 3.0168          |
-| 0.8081        | 1.7813 | 105  | 3.0335          |
-| 0.8227        | 2.0361 | 120  | 3.0369          |
-| 0.7416        | 2.2909 | 135  | 3.0876          |
-| 0.7396        | 2.5456 | 150  | 3.1023          |
-| 0.7775        | 2.8004 | 165  | 3.0986          |
-
-
-### Framework versions
-
-- Transformers 4.45.1
-- Pytorch 2.4.0+cu121
-- Datasets 2.21.0
-- Tokenizers 0.20.2
+</details><br>