Update README.md

README.md

@@ -5,7 +5,54 @@ tags:
 model-index:
 - name: EVA-Qwen2.5-1.5B-FFT-v0.0
   results: []
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-1.5B
+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
 ---
+# EVA Qwen2.5-1.5BB v0.0
+
+<p>
+  A small-scale RP/storywriting specialist model, full-parameter finetune of Qwen2.5-1.5B 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>
+  Unlike EVA-D 1.5B v0.0, this model was created without using DistillKit, and unlike other versions of EVA, Spectrum wasn't used either, since layer freezing is inefficient at small scale.
+</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>TBA hours on 4x3090Ti</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>to Gryphe, Lemmy, Kalomaze, Nopm, Epiculous and CognitiveComputations for the data</li>
+  <li>and to Allura-org for support, feedback, beta-testing and doing quality control of EVA models.</li></ul>
 
 Model card in progress making public for benchmarks.
 
@@ -132,66 +179,4 @@ weight_decay: 0.15
 
 ```
 
-</details><br>
-
-# EVA-Qwen2.5-1.5B-FFT-v0.0
-
-This model was trained from scratch on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 1.3685
-
-## 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-06
-- train_batch_size: 1
-- eval_batch_size: 1
-- seed: 42
-- distributed_type: multi-GPU
-- num_devices: 4
-- gradient_accumulation_steps: 8
-- total_train_batch_size: 32
-- total_eval_batch_size: 4
-- 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 |
-|:-------------:|:------:|:----:|:---------------:|
-| 1.8166        | 0.0028 | 1    | 1.6772          |
-| 1.7031        | 0.2519 | 89   | 1.4633          |
-| 1.5925        | 0.5037 | 178  | 1.4171          |
-| 1.512         | 0.7556 | 267  | 1.3993          |
-| 1.5122        | 1.0050 | 356  | 1.3888          |
-| 1.5281        | 1.2574 | 445  | 1.3825          |
-| 1.4895        | 1.5099 | 534  | 1.3775          |
-| 1.4599        | 1.7624 | 623  | 1.3731          |
-| 1.4754        | 2.0103 | 712  | 1.3705          |
-| 1.4841        | 2.2619 | 801  | 1.3696          |
-| 1.4861        | 2.5136 | 890  | 1.3689          |
-| 1.5258        | 2.7653 | 979  | 1.3685          |
-
-
-### Framework versions
-
-- Transformers 4.45.2
-- Pytorch 2.5.1+cu124
-- Datasets 2.21.0
-- Tokenizers 0.20.3
+</details><br>