deepakkarkala
/

gemma3_1b_lora_sft_sitcom

@@ -1,202 +1,113 @@
 ---
-base_model: google/gemma-3-1b-it
 library_name: peft
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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-- **Developed by:** [More Information Needed]
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-## Uses
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-## How to Get Started with the Model
-Use the code below to get started with the model.
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-#### Training Hyperparameters
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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 ### Framework versions
-- PEFT 0.15.2

 ---
 library_name: peft
+license: gemma
+base_model: google/gemma-3-1b-it
+tags:
+- axolotl
+- generated_from_trainer
+model-index:
+- name: gemma3_1b_lora_sft_sitcom
+  results: []
 ---
+<!-- 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. -->
+[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
+<details><summary>See axolotl config</summary>
+axolotl version: `0.10.0.dev0`
+```yaml
+adapter: qlora
+base_model: google/gemma-3-1b-it
+bf16: auto
+chat_template: gemma3
+datasets:
+- path: deepakkarkala/sft_sitcom_chandlerbing_jsonl
+  split: train_without_fewshots
+  type: alpaca
+ddp_find_unused_parameters: true
+eval_sample_packing: false
+evals_per_epoch: null
+flash_attention: true
+gradient_accumulation_steps: 8
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+hub_model_id: deepakkarkala/gemma3_1b_lora_sft_sitcom
+learning_rate: 0.0002
+load_in_4bit: true
+load_in_8bit: false
+logging_steps: 1
+lora_alpha: 16
+lora_dropout: 0.05
+lora_r: 32
+lora_target_linear: true
+lr_scheduler: cosine
+micro_batch_size: 2
+model_type: AutoModelForCausalLM
+num_epochs: 4
+optimizer: adamw_bnb_8bit
+output_dir: ./outputs/out
+pad_to_sequence_len: true
+resume_from_checkpoint: null
+sample_packing: true
+saves_per_epoch: 1
+sequence_len: 2048
+special_tokens: null
+tf32: true
+tokenizer_type: AutoTokenizer
+val_set_size: 0.05
+wandb_entity: deepakkarkala-personal
+wandb_log_model: checkpoint
+wandb_name: sft_gemma3_1b
+wandb_project: finetuning_llama31_8b_sitcom
+wandb_run_id: sft_gemma3_1b_2
+wandb_watch: null
+warmup_ratio: 0.1
+weight_decay: 0.0
+```
+</details><br>
+[<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/deepakkarkala-personal/finetuning_llama31_8b_sitcom/runs/sft_gemma3_1b_2)
+# gemma3_1b_lora_sft_sitcom
+This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) on an unknown dataset.
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 16
+- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 26
+- training_steps: 264
 ### Framework versions
+- PEFT 0.15.2
+- Transformers 4.51.3
+- Pytorch 2.6.0+cu124
+- Datasets 3.5.1
+- Tokenizers 0.21.1