Update README.md

README.md

@@ -6,19 +6,75 @@ tags:
 - axolotl
 - generated_from_trainer
 model-index:
-- name: Llama-3.1-8B-Magpie-SFT-GMix-550K
+- name: MagpieLM-8B-SFT-v0.1
   results: []
+datasets:
+- Magpie-Align/MagpieLM-SFT-Data-v0.1
 ---
 
-<!-- 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](https://cdn-uploads.huggingface.co/production/uploads/653df1323479e9ebbe3eb6cc/FWWILXrAGNwWr52aghV0S.png)
+
+# 🐦 MagpieLM-8B-SFT-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
+
+*Model full name: Llama3.1-MagpieLM-8B-SFT-v0.1*
+
+This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on [Magpie-Align/MagpieLM-SFT-Data-v0.1](https://huggingface.co/datasets/Magpie-Align/MagpieLM-SFT-Data-v0.1) dataset.
+
+## 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: 32
+- total_train_batch_size: 128
+- 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: 51
+- num_epochs: 2
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 0.9311        | 0.0038 | 1    | 0.9847          |
+| 0.561         | 0.2015 | 53   | 0.5765          |
+| 0.4843        | 0.4030 | 106  | 0.5039          |
+| 0.4608        | 0.6045 | 159  | 0.4814          |
+| 0.4454        | 0.8060 | 212  | 0.4678          |
+| 0.4403        | 1.0075 | 265  | 0.4596          |
+| 0.3965        | 1.1938 | 318  | 0.4574          |
+| 0.3952        | 1.3953 | 371  | 0.4554          |
+| 0.3962        | 1.5968 | 424  | 0.4547          |
+| 0.3948        | 1.7983 | 477  | 0.4544          |
+
+
+### Framework versions
+
+- Transformers 4.45.0.dev0
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1
 
 [<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: meta-llama/Meta-Llama-3.1-8B
 model_type: LlamaForCausalLM
 tokenizer_type: AutoTokenizer
@@ -30,18 +86,13 @@ strict: false
 main_process_port: 0
 
 datasets:
-  - path: flydust/Magpie-Llama-3-70B-300K-Gemma2-9B
+  - path: Magpie-Align/MagpieLM-SFT-Data-v0.1
     type: sharegpt
     conversation: llama3
-  - path: flydust/Magpie-Reasoning-150K-Gemma2-9B
-    type: sharegpt
-    conversation: llama3
-  - path: flydust/Magpie-100k-Gemma2-9B
-    type: sharegpt
-    conversation: llama3
-dataset_prepared_path: /data/zhangchen_xu/last_run_prepared
+
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.001
-output_dir: /data/zhangchen_xu/axolotl_out/Llama-3.1-8B-SFT-GMix-550K
+output_dir: axolotl_out/MagpieLM-8B-SFT-v0.1
 
 sequence_len: 8192
 sample_packing: true
@@ -51,9 +102,9 @@ pad_to_sequence_len: true
 wandb_project: SynDa
 wandb_entity:
 wandb_watch:
-wandb_name: Llama-3.1-8B-Mix-SFT-GMix-550K
+wandb_name: MagpieLM-8B-SFT-v0.1
 wandb_log_model:
-hub_model_id: Magpie-Align/Llama-3.1-8B-Magpie-SFT-GMix-550K
+hub_model_id: Magpie-Align/MagpieLM-8B-SFT-v0.1
 
 gradient_accumulation_steps: 32
 micro_batch_size: 1
@@ -90,65 +141,4 @@ special_tokens:
   pad_token: <|end_of_text|>
 
 ```
-
-</details><br>
-
-# Llama-3.1-8B-Magpie-SFT-GMix-550K
-
-This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.4544
-
-## 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: 32
-- total_train_batch_size: 128
-- 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: 51
-- num_epochs: 2
-
-### Training results
-
-| Training Loss | Epoch  | Step | Validation Loss |
-|:-------------:|:------:|:----:|:---------------:|
-| 0.9311        | 0.0038 | 1    | 0.9847          |
-| 0.561         | 0.2015 | 53   | 0.5765          |
-| 0.4843        | 0.4030 | 106  | 0.5039          |
-| 0.4608        | 0.6045 | 159  | 0.4814          |
-| 0.4454        | 0.8060 | 212  | 0.4678          |
-| 0.4403        | 1.0075 | 265  | 0.4596          |
-| 0.3965        | 1.1938 | 318  | 0.4574          |
-| 0.3952        | 1.3953 | 371  | 0.4554          |
-| 0.3962        | 1.5968 | 424  | 0.4547          |
-| 0.3948        | 1.7983 | 477  | 0.4544          |
-
-
-### Framework versions
-
-- Transformers 4.45.0.dev0
-- Pytorch 2.3.0+cu121
-- Datasets 2.19.1
-- Tokenizers 0.19.1
+</details><br>