End of training

README.md

@@ -1,58 +1,57 @@
 ---
+library_name: peft
+license: apache-2.0
 base_model: PY007/TinyLlama-1.1B-Chat-v0.3
-library_name: transformers
-model_name: model_colors_lora
 tags:
 - generated_from_trainer
-- trl
-- sft
-licence: license
+model-index:
+- name: model_colors_lora
+  results: []
 ---
 
-# Model Card for model_colors_lora
+<!-- 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 [PY007/TinyLlama-1.1B-Chat-v0.3](https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+# model_colors_lora
 
-## Quick start
+This model is a fine-tuned version of [PY007/TinyLlama-1.1B-Chat-v0.3](https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3) on the None 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="knarayan/model_colors_lora", 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/keshi8086-palo-alto-networks/huggingface/runs/smacpsw8) 
+More information needed
 
+## Training and evaluation data
 
-This model was trained with SFT.
+More information needed
 
-### Framework versions
+## Training procedure
 
-- TRL: 0.15.0
-- Transformers: 4.48.2
-- Pytorch: 2.5.1+cu124
-- Datasets: 3.2.0
-- Tokenizers: 0.21.0
+### Training hyperparameters
 
-## Citations
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- 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
+- training_steps: 200
+- 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 Gallouédec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
-```
+
+### Framework versions
+
+- PEFT 0.14.0
+- Transformers 4.48.2
+- Pytorch 2.5.1+cu124
+- Datasets 3.2.0
+- Tokenizers 0.21.0

runs/Feb13_20-03-50_664c4e247bf4/events.out.tfevents.1739477031.664c4e247bf4.1574.5

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