Update README.md

README.md

@@ -1,3 +1,88 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- m-a-p/CodeFeedback-Filtered-Instruction
+- m-a-p/Code-Feedback
+language:
+- en
+library_name: transformers
+tags:
+- llama-factory
+- unsloth
+---
+# h2o-danube2 with ChatML template
+
+This model was first fine-tuned with [BAdam](https://arxiv.org/abs/2404.02827 "BAdam: A Memory Efficient Full Parameter Optimization Method for Large Language Models") on [m-a-p/CodeFeedback-Filtered-Instruction](https://huggingface.co/datasets/m-a-p/CodeFeedback-Filtered-Instruction) and [m-a-p/Code-Feedback](https://huggingface.co/datasets/m-a-p/Code-Feedback), unfiltered from the latest [dolphin dataset](https://huggingface.co/datasets/cognitivecomputations/dolphin-2.9.3), using LLama-Factory.
+
+## Template
+
+```jinja
+<|im_start|>system
+{{system}}<|im_end|>
+<|im_start|>user
+{{instruction}}<|im_end|>
+<|im_start|>assistant
+{{response}}<|im_end|>
+```
+
+## BAdam config
+
+**System:** You are a helpful coding assistant.
+
+```yaml
+### model
+model_name_or_path: danube2-base-chatml
+
+### method
+stage: sft
+do_train: true
+finetuning_type: full
+use_badam: true
+badam_switch_mode: ascending
+badam_switch_interval: 50
+badam_verbose: 1
+badam_start_block: 10
+seed: 720
+
+### dataset
+dataset: codefeedback_instruct_unfiltered,codefeedback_unfiltered
+template: hermes_chatml
+cutoff_len: 8192
+overwrite_cache: false
+preprocessing_num_workers: 12
+
+### output
+output_dir: code-feedback-chatml-badam
+logging_steps: 5
+save_steps: 1
+save_strategy: epoch
+plot_loss: true
+overwrite_output_dir: false
+
+### train
+per_device_train_batch_size: 2
+gradient_accumulation_steps: 8
+learning_rate: 0.00001
+num_train_epochs: 1
+lr_scheduler_type: cosine
+warmup_ratio: 0.01
+bf16: true
+flash_attn: fa2
+
+### eval
+val_size: 0.01
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 2000
+```
+
+### BAdam training results
+
+| Training Loss | Epoch  | Step  | Validation Loss |
+|:-------------:|:------:|:-----:|:---------------:|
+| 0.6181        | 0.1789 | 2000  | 0.6044          |
+| 0.6835        | 0.3578 | 4000  | 0.5949          |
+| 0.5649        | 0.5367 | 6000  | 0.5893          |
+| 0.6559        | 0.7155 | 8000  | 0.5850          |
+| 0.6591        | 0.8944 | 10000 | 0.5839          |
+