Update README.md

README.md

@@ -5,12 +5,61 @@ tags:
 - axolotl
 - generated_from_trainer
 model-index:
-- name: Llama-3-8B-SynDa-70BQA-200K-Filtered-L
+- name: Llama-3-8B-Magpie-Pro-SFT-200K-v0.1
   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. -->
+# Magpie-Align/Llama-3-8B-Magpie-Pro-SFT-200K-v0.1
+
+Project Web: [https://magpie-align.github.io/](https://magpie-align.github.io/)
+
+Arxiv Technical Report: [https://arxiv.org/abs/2406.08464](https://arxiv.org/abs/2406.08464)
+
+Codes: [https://github.com/magpie-align/magpie](https://github.com/magpie-align/magpie)
+
+## About This Model
+
+This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on **First 200K data** of [Magpie-Align/Magpie-Pro-300K-Filtered](https://huggingface.co/datasets/Magpie-Align/Magpie-Pro-300K-Filtered) dataset.
+
+Please use [Magpie-Align/Llama-3-8B-Magpie-Pro-SFT-v0.1](https://huggingface.co/Magpie-Align/Llama-3-8B-Magpie-Pro-SFT-v0.1) with better performance.
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- 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: 100
+- num_epochs: 2
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 0.8686        | 0.0018 | 1    | 0.8670          |
+| 0.514         | 0.3342 | 184  | 0.5190          |
+| 0.4769        | 0.6685 | 368  | 0.4684          |
+| 0.4394        | 1.0027 | 552  | 0.4440          |
+| 0.3399        | 1.3224 | 736  | 0.4436          |
+| 0.3394        | 1.6567 | 920  | 0.4413          |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1
 
 [<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)
 <details><summary>See axolotl config</summary>
@@ -26,25 +75,18 @@ load_in_4bit: false
 strict: false
 
 datasets:
-  - path: SynDa/Llama-3-70B-SynDa-200K-Filtered-L
+  - path: Magpie-Align/Magpie-Pro-300K-Filtered-First200K
     type: sharegpt
     conversation: llama3
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.001
-output_dir: ./out_Llama-3-70B-SynDa-200K-Filtered-L
+output_dir: ./out_Llama-3-8B-Magpie-Pro-200K-FilteredL
 
 sequence_len: 8192
 sample_packing: true
 eval_sample_packing: false
 pad_to_sequence_len: true
 
-wandb_project: SynDa
-wandb_entity:
-wandb_watch:
-wandb_name: Llama-3-70B-SynDa-200K-FilteredL-2EP-FFT
-wandb_log_model:
-hub_model_id: SynDa/Llama-3-8B-SynDa-70BQA-200K-Filtered-L
-
 gradient_accumulation_steps: 8
 micro_batch_size: 1
 num_epochs: 2
@@ -81,60 +123,4 @@ special_tokens:
 
 ```
 
-</details><br>
-
-# Llama-3-8B-SynDa-70BQA-200K-Filtered-L
-
-This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.4413
-
-## 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: 2e-05
-- 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: 100
-- num_epochs: 2
-
-### Training results
-
-| Training Loss | Epoch  | Step | Validation Loss |
-|:-------------:|:------:|:----:|:---------------:|
-| 0.8686        | 0.0018 | 1    | 0.8670          |
-| 0.514         | 0.3342 | 184  | 0.5190          |
-| 0.4769        | 0.6685 | 368  | 0.4684          |
-| 0.4394        | 1.0027 | 552  | 0.4440          |
-| 0.3399        | 1.3224 | 736  | 0.4436          |
-| 0.3394        | 1.6567 | 920  | 0.4413          |
-
-
-### Framework versions
-
-- Transformers 4.40.2
-- Pytorch 2.3.0+cu121
-- Datasets 2.19.1
-- Tokenizers 0.19.1
+</details><br>