Create README.md

README.md

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+---
+license: apache-2.0
+library_name: transformers
+tags:
+- generated_from_trainer
+base_model: google/gemma-2b
+model-index:
+- name: out1v
+  results: []
+pipeline_tag: text-generation
+---
+
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
+<details><summary>See axolotl config</summary>
+
+axolotl version: `0.4.0`
+```yaml
+# use google/gemma-7b if you have access
+base_model: google/gemma-2b
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+#lora_model_dir: 
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+# huggingface repo
+datasets:
+  - path: ./python-oasst/cleaned-dataset.jsonl
+    type: oasst
+val_set_size: 0.20
+output_dir: ./out1v
+
+adapter: lora
+lora_r: 8
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+gptq: false
+sequence_len: 4096
+sample_packing: false
+pad_to_sequence_len: true
+
+wandb_project: gemma-2b-it
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+
+gradient_accumulation_steps: 4
+micro_batch_size: 4
+num_epochs: 2
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: true
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 4
+eval_table_size:
+eval_max_new_tokens: 128
+saves_per_epoch: 1
+debug:
+deepspeed: deepspeed_configs/zero3_bf16.json
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+
+```
+
+</details><br>
+
+# out1v
+
+This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.1243
+
+## 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: 4
+- eval_batch_size: 4
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 2
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- total_eval_batch_size: 8
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 1045
+- num_epochs: 2
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss |
+|:-------------:|:-----:|:-----:|:---------------:|
+| 1.9865        | 0.0   | 1     | 2.2808          |
+| 1.1033        | 0.25  | 2613  | 1.1338          |
+| 1.0035        | 0.5   | 5226  | 1.1308          |
+| 1.0089        | 0.75  | 7839  | 1.1272          |
+| 0.9816        | 1.0   | 10452 | 1.1238          |
+| 0.8727        | 1.25  | 13065 | 1.1270          |
+| 0.9951        | 1.5   | 15678 | 1.1254          |
+| 1.01          | 1.75  | 18291 | 1.1242          |
+| 1.0677        | 2.0   | 20904 | 1.1243          |
+
+
+### Framework versions
+
+- PEFT 0.10.0
+- Transformers 4.40.0.dev0
+- Pytorch 2.2.2+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.0