Update README.md

README.md

@@ -8,13 +8,38 @@ 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. -->
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/658a46cbfb9c2bdfae75b3a6/9ZBUlmzDCnNmQEdUUbyEL.png)
+
+This is the 10th in a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet and Opus.
+
+This model is fine-tuned on top of [google/gemma-2-9b](https://huggingface.co/google/gemma-2-9b).
+
+## Prompting
+Model has been Instruct tuned with the customgemma2 formatting. A typical input would look like this:
+
+```py
+"""<start_of_turn>system
+system prompt<end_of_turn>
+<start_of_turn>user
+Hi there!<end_of_turn>
+<start_of_turn>model
+Nice to meet you!<end_of_turn>
+<start_of_turn>user
+Can I ask a question?<end_of_turn>
+<start_of_turn>model
+"""
+```
+
+## SillyTavern templates
+
+WIP
+
+</details><br>
+
+## Axolotl config
 
-[<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: `0.4.1`
 ```yaml
 base_model: google/gemma-2-9b
 model_type: AutoModelForCausalLM
@@ -96,51 +121,23 @@ fsdp:
 fsdp_config:
 special_tokens:
 ```
-
 </details><br>
 
-# magnum-v3-9b-customgemma2
-
-This model is a fine-tuned version of [google/gemma-2-9b](https://huggingface.co/google/gemma-2-9b) on the None dataset.
-
-## 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: 6e-06
-- train_batch_size: 1
-- eval_batch_size: 1
-- seed: 42
-- distributed_type: multi-GPU
-- num_devices: 8
-- gradient_accumulation_steps: 8
-- total_train_batch_size: 64
-- total_eval_batch_size: 8
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_steps: 50
-- num_epochs: 2
+## Credits
+We'd like to thank Recursal / Featherless for sponsoring the compute for this train, Featherless has been hosting our Magnum models since the first 72 B and has given thousands of people access to our models and helped us grow.
 
-### Training results
+We would also like to thank all members of Anthracite who made this finetune possible. 
 
+- [anthracite-org/stheno-filtered-v1.1](https://huggingface.co/datasets/anthracite-org/stheno-filtered-v1.1)
+- [anthracite-org/kalo-opus-instruct-22k-no-refusal](https://huggingface.co/datasets/anthracite-org/kalo-opus-instruct-22k-no-refusal)
+- [anthracite-org/nopm_claude_writing_fixed](https://huggingface.co/datasets/anthracite-org/nopm_claude_writing_fixed)
+- [Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned](https://huggingface.co/datasets/Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned)
+- [Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned](https://huggingface.co/datasets/Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned)
 
+## Training
+The training was done for 2 epochs. We used  8x[H100s](https://www.nvidia.com/en-us/data-center/h100/) GPUs graciously provided by [Recursal AI](https://recursal.ai/) / [Featherless AI](https://featherless.ai/) for the full-parameter fine-tuning of the model.
 
-### Framework versions
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
 
-- Transformers 4.44.0
-- Pytorch 2.4.0+cu121
-- Datasets 2.20.0
-- Tokenizers 0.19.1
+## Safety
+...