End of training

README.md

@@ -1,58 +1,55 @@
----
-base_model: meta-llama/Llama-3.1-8B-Instruct
-library_name: transformers
-model_name: Llama3_CAPL_Model
-tags:
-- generated_from_trainer
-- trl
-- sft
-licence: license
----
+---
+library_name: peft
+license: llama3.2
+base_model: meta-llama/Llama-3.2-1B
+tags:
+- generated_from_trainer
+model-index:
+- name: Llama3_CAPL_Model
+  results: []
+---
 
-# Model Card for Llama3_CAPL_Model
+<!-- 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 [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+# Llama3_CAPL_Model
 
-## Quick start
+This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset.
 
-```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="DJseo/Llama3_CAPL_Model", 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
 
-- TRL: 0.15.2
-- Transformers: 4.49.0
-- Pytorch: 2.5.1+cu121
-- Datasets: 3.4.1
-- Tokenizers: 0.21.1
+### Training hyperparameters
 
-## Citations
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 4
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 3
+- mixed_precision_training: Native AMP
 
+### Training results
 
 
-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 Gallou챕dec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
-```
+
+### Framework versions
+
+- PEFT 0.14.0
+- Transformers 4.49.0
+- Pytorch 2.5.1+cu121
+- Datasets 3.4.1
+- Tokenizers 0.21.1