End of training

README.md

@@ -1,57 +1,71 @@
 ---
+library_name: peft
+license: llama3.2
 base_model: meta-llama/Llama-3.2-1B
-library_name: transformers
-model_name: Llama-3.2-1B-Summarization-QLoRa
 tags:
 - generated_from_trainer
-- trl
-- sft
-licence: license
+datasets:
+- scitldr
+model-index:
+- name: Llama-3.2-1B-Summarization-LoRa
+  results: []
 ---
 
-# Model Card for Llama-3.2-1B-Summarization-QLoRa
+<!-- 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-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+# Llama-3.2-1B-Summarization-LoRa
 
-## 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 the scitldr dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.5661
 
-```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="pkbiswas/Llama-3.2-1B-Summarization-QLoRa", 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
 
-[<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/pkbiswas-verizon/huggingface/runs/tnejvjab)
+More information needed
 
-This model was trained with SFT.
+## Training and evaluation data
 
-### Framework versions
+More information needed
+
+## Training procedure
 
-- TRL: 0.12.1
-- Transformers: 4.46.2
-- Pytorch: 2.5.1+cu121
-- Datasets: 3.1.0
-- Tokenizers: 0.20.3
+### Training hyperparameters
 
-## Citations
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- optimizer: Use paged_adamw_32bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 2
+- num_epochs: 2
+- mixed_precision_training: Native AMP
 
+### Training results
 
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 2.45          | 0.2008 | 200  | 2.5272          |
+| 2.4331        | 0.4016 | 400  | 2.5327          |
+| 2.4369        | 0.6024 | 600  | 2.5285          |
+| 2.4315        | 0.8032 | 800  | 2.5238          |
+| 2.4303        | 1.0040 | 1000 | 2.5181          |
+| 2.1077        | 1.2048 | 1200 | 2.5525          |
+| 2.0951        | 1.4056 | 1400 | 2.5611          |
+| 2.0738        | 1.6064 | 1600 | 2.5591          |
+| 2.0539        | 1.8072 | 1800 | 2.5661          |
+
+
+### 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 Gallouédec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
-```
+- PEFT 0.13.2
+- Transformers 4.46.2
+- Pytorch 2.5.1+cu121
+- Datasets 3.1.0
+- Tokenizers 0.20.3

adapter_config.json

@@ -20,13 +20,13 @@
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
-    "q_proj",
+    "o_proj",
+    "v_proj",
+    "gate_proj",
     "up_proj",
     "down_proj",
-    "k_proj",
-    "v_proj",
-    "o_proj",
-    "gate_proj"
+    "q_proj",
+    "k_proj"
   ],
   "task_type": "CAUSAL_LM",
   "use_dora": false,

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training_args.bin

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