Model save

README.md

@@ -1,58 +1,60 @@
 ---
+library_name: peft
+license: llama3.1
 base_model: meta-llama/Meta-Llama-3.1-8B
-library_name: transformers
-model_name: summarization
 tags:
-- generated_from_trainer
+- base_model:adapter:meta-llama/Meta-Llama-3.1-8B
+- lora
 - sft
+- transformers
 - trl
-licence: license
+pipeline_tag: text-generation
+model-index:
+- name: summarization
+  results: []
 ---
 
-# Model Card for summarization
+<!-- 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/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/calmm-hanoi-university-of-science/summarization/runs/7kmcsnsu)
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/calmm-hanoi-university-of-science/summarization/runs/7kmcsnsu)
+# summarization
 
-## Quick start
+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 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="calmm-m/summarization", 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
-
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/calmm-hanoi-university-of-science/summarization/runs/7kmcsnsu) 
+## Intended uses & limitations
 
+More information needed
 
-This model was trained with SFT.
+## Training and evaluation data
 
-### Framework versions
+More information needed
 
-- TRL: 0.24.0
-- Transformers: 4.57.1
-- Pytorch: 2.6.0+cu124
-- Datasets: 4.3.0
-- Tokenizers: 0.22.1
+## Training procedure
 
-## Citations
+### Training hyperparameters
 
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 1
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 8
+- optimizer: Use OptimizerNames.PAGED_ADAMW with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.03
+- training_steps: 200
 
+### Framework versions
 
-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 Gallou{\'e}dec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
-```
+- PEFT 0.17.1
+- Transformers 4.57.1
+- Pytorch 2.6.0+cu124
+- Datasets 4.3.0
+- Tokenizers 0.22.1

runs/Oct30_20-20-52_1dd01875256c/events.out.tfevents.1761855668.1dd01875256c.37.1

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