End of training

README.md

@@ -1,58 +1,94 @@
 ---
-base_model: rasyosef/Llama-3.2-400M-Amharic
 library_name: transformers
-model_name: Llama-3.2-400M-Amharic-Poems-Stories-V5
+base_model: rasyosef/Llama-3.2-400M-Amharic
 tags:
-- generated_from_trainer
 - trl
 - sft
-licence: license
+- generated_from_trainer
+model-index:
+- name: Llama-3.2-400M-Amharic-Poems-Stories-V5
+  results: []
 ---
 
-# Model Card for Llama-3.2-400M-Amharic-Poems-Stories-V5
+<!-- 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. -->
 
-This model is a fine-tuned version of [rasyosef/Llama-3.2-400M-Amharic](https://huggingface.co/rasyosef/Llama-3.2-400M-Amharic).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+# Llama-3.2-400M-Amharic-Poems-Stories-V5
 
-## Quick start
+This model is a fine-tuned version of [rasyosef/Llama-3.2-400M-Amharic](https://huggingface.co/rasyosef/Llama-3.2-400M-Amharic) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.2025
+- Model Preparation Time: 0.003
 
-```python
-from transformers import pipeline
+## Model description
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="yosefw/Llama-3.2-400M-Amharic-Poems-Stories-V5", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
+More information needed
 
-## Training procedure
+## Intended uses & limitations
 
- 
+More information needed
 
+## Training and evaluation data
 
-This model was trained with SFT.
+More information needed
 
-### Framework versions
+## Training procedure
+
+### Training hyperparameters
 
-- TRL: 0.15.2
-- Transformers: 4.49.0
-- Pytorch: 2.4.1+cu121
-- Datasets: 3.3.2
-- Tokenizers: 0.21.0
+The following hyperparameters were used during training:
+- learning_rate: 8e-05
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- lr_scheduler_warmup_steps: 250
+- num_epochs: 4
+- mixed_precision_training: Native AMP
 
-## Citations
+### Training results
 
+| Training Loss | Epoch  | Step  | Validation Loss | Model Preparation Time |
+|:-------------:|:------:|:-----:|:---------------:|:----------------------:|
+| 3.0159        | 0.1219 | 500   | 2.4049          | 0.003                  |
+| 2.0028        | 0.2437 | 1000  | 1.6978          | 0.003                  |
+| 1.5634        | 0.3656 | 1500  | 1.4614          | 0.003                  |
+| 1.4024        | 0.4874 | 2000  | 1.3237          | 0.003                  |
+| 1.2445        | 0.6093 | 2500  | 1.2619          | 0.003                  |
+| 1.3169        | 0.7312 | 3000  | 1.2383          | 0.003                  |
+| 1.2045        | 0.8530 | 3500  | 1.2104          | 0.003                  |
+| 1.2074        | 0.9749 | 4000  | 1.2025          | 0.003                  |
+| 0.715         | 1.0968 | 4500  | 1.3746          | 0.003                  |
+| 0.5526        | 1.2186 | 5000  | 1.3882          | 0.003                  |
+| 0.5383        | 1.3405 | 5500  | 1.3509          | 0.003                  |
+| 0.5541        | 1.4623 | 6000  | 1.3664          | 0.003                  |
+| 0.561         | 1.5842 | 6500  | 1.3486          | 0.003                  |
+| 0.5278        | 1.7061 | 7000  | 1.3447          | 0.003                  |
+| 0.5415        | 1.8279 | 7500  | 1.3282          | 0.003                  |
+| 0.5491        | 1.9498 | 8000  | 1.3404          | 0.003                  |
+| 0.3309        | 2.0717 | 8500  | 1.5191          | 0.003                  |
+| 0.1989        | 2.1935 | 9000  | 1.5328          | 0.003                  |
+| 0.2018        | 2.3154 | 9500  | 1.5266          | 0.003                  |
+| 0.1954        | 2.4372 | 10000 | 1.5309          | 0.003                  |
+| 0.1957        | 2.5591 | 10500 | 1.5363          | 0.003                  |
+| 0.1983        | 2.6810 | 11000 | 1.5228          | 0.003                  |
+| 0.1962        | 2.8028 | 11500 | 1.5295          | 0.003                  |
+| 0.1934        | 2.9247 | 12000 | 1.5303          | 0.003                  |
+| 0.1566        | 3.0466 | 12500 | 1.5726          | 0.003                  |
+| 0.1045        | 3.1684 | 13000 | 1.5975          | 0.003                  |
+| 0.1071        | 3.2903 | 13500 | 1.5947          | 0.003                  |
+| 0.1076        | 3.4121 | 14000 | 1.5920          | 0.003                  |
+| 0.1068        | 3.5340 | 14500 | 1.5922          | 0.003                  |
+| 0.1052        | 3.6559 | 15000 | 1.5921          | 0.003                  |
+| 0.1074        | 3.7777 | 15500 | 1.5922          | 0.003                  |
+| 0.1084        | 3.8996 | 16000 | 1.5923          | 0.003                  |
 
 
-Cite TRL as:
-    
-```bibtex
-@misc{vonwerra2022trl,
-	title        = {{TRL: Transformer Reinforcement Learning}},
-	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
-```
+### Framework versions
+
+- Transformers 4.45.0
+- Pytorch 2.4.1+cu121
+- Datasets 3.3.2
+- Tokenizers 0.20.3

generation_config.json

@@ -3,5 +3,5 @@
   "bos_token_id": 32000,
   "eos_token_id": 32001,
   "pad_token_id": 32001,
-  "transformers_version": "4.49.0"
+  "transformers_version": "4.45.0"
 }

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