Model save

README.md

@@ -1,57 +1,73 @@
 ---
+library_name: peft
+license: gemma
 base_model: google/gemma-7b
-library_name: transformers
-model_name: gemma7b-lora-alpaca-11-v1
 tags:
-- generated_from_trainer
 - trl
 - sft
-licence: license
+- generated_from_trainer
+datasets:
+- generator
+model-index:
+- name: gemma7b-lora-alpaca-11-v1
+  results: []
 ---
 
-# Model Card for gemma7b-lora-alpaca-11-v1
+<!-- 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 [google/gemma-7b](https://huggingface.co/google/gemma-7b).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+# gemma7b-lora-alpaca-11-v1
 
-## Quick start
+This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) on the generator dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.6643
 
-```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="klcsp/gemma7b-lora-alpaca-11-v1", 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: 0.12.1
-- Transformers: 4.46.2
-- Pytorch: 2.3.1+cu121
-- Datasets: 3.1.0
-- Tokenizers: 0.20.3
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 8
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 128
+- total_eval_batch_size: 64
+- 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: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 5
 
-## Citations
+### Training results
 
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 2.9056        | 0.9924 | 65   | 2.6113          |
+| 1.8271        | 2.0    | 131  | 1.8230          |
+| 1.7019        | 2.9924 | 196  | 1.7041          |
+| 1.7024        | 4.0    | 262  | 1.6962          |
+| 1.6463        | 4.9618 | 325  | 1.6643          |
 
 
-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
+
+- PEFT 0.13.2
+- Transformers 4.46.2
+- Pytorch 2.3.1+cu121
+- Datasets 3.1.0
+- Tokenizers 0.20.3

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