Remove README.md

README.md

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----
-library_name: peft
-license: mit
-base_model: fxmarty/really-tiny-falcon-testing
-tags:
-- axolotl
-- generated_from_trainer
-model-index:
-- name: 9253ae74f1
-  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: `0.4.1`
-```yaml
-adapter: lora
-base_model: fxmarty/really-tiny-falcon-testing
-bf16: auto
-chat_template: llama3
-cosine_min_lr_ratio: 0.1
-data_processes: 16
-dataset_prepared_path: null
-datasets:
-- data_files:
-  - 16ac60e013b1863e_train_data.json
-  ds_type: json
-  format: custom
-  path: /workspace/input_data/16ac60e013b1863e_train_data.json
-  type:
-    field_input: Molecule Sequence
-    field_instruction: Molecule ID
-    field_output: Protein Sequence
-    format: '{instruction} {input}'
-    no_input_format: '{instruction}'
-    system_format: '{system}'
-    system_prompt: ''
-debug: null
-deepspeed: null
-device_map: '{'''':torch.cuda.current_device()}'
-do_eval: true
-early_stopping_patience: 1
-eval_batch_size: 8
-eval_sample_packing: false
-eval_steps: 25
-evaluation_strategy: steps
-flash_attention: false
-fp16: null
-fsdp: null
-fsdp_config: null
-gradient_accumulation_steps: 16
-gradient_checkpointing: true
-group_by_length: true
-hub_model_id: magatex/9253ae74f1
-hub_repo: stevemonite
-hub_strategy: checkpoint
-hub_token: null
-learning_rate: 0.0003
-load_in_4bit: false
-load_in_8bit: false
-local_rank: null
-logging_steps: 1
-lora_alpha: 32
-lora_dropout: 0.05
-lora_fan_in_fan_out: null
-lora_model_dir: null
-lora_r: 16
-lora_target_linear: true
-lora_target_modules:
-- q_proj
-- v_proj
-lr_scheduler: cosine
-max_grad_norm: 1.0
-max_memory:
-  0: 70GiB
-max_steps: 38
-micro_batch_size: 8
-mlflow_experiment_name: /tmp/16ac60e013b1863e_train_data.json
-model_type: AutoModelForCausalLM
-num_epochs: 2
-optim_args:
-  adam_beta1: 0.9
-  adam_beta2: 0.95
-  adam_epsilon: 1e-5
-optimizer: adamw_torch
-output_dir: miner_id_24
-pad_to_sequence_len: true
-resume_from_checkpoint: null
-s2_attention: null
-sample_packing: false
-save_steps: 50
-save_strategy: steps
-sequence_len: 2048
-strict: false
-tf32: false
-tokenizer_type: AutoTokenizer
-torch_compile: false
-train_on_inputs: false
-trust_remote_code: true
-val_set_size: 50
-wandb_entity: null
-wandb_mode: online
-wandb_project: Public_TuningSN
-wandb_run: miner_id_24
-wandb_runid: 53ae74f1
-warmup_raio: 0.03
-warmup_ratio: 0.03
-weight_decay: 0.01
-xformers_attention: null
-
-```
-
-</details><br>
-
-# 9253ae74f1
-
-This model is a fine-tuned version of [fxmarty/really-tiny-falcon-testing](https://huggingface.co/fxmarty/really-tiny-falcon-testing) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 11.0496
-
-## 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: 0.0003
-- train_batch_size: 8
-- eval_batch_size: 8
-- seed: 42
-- gradient_accumulation_steps: 16
-- total_train_batch_size: 128
-- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_steps: 2
-- training_steps: 38
-
-### Training results
-
-| Training Loss | Epoch  | Step | Validation Loss |
-|:-------------:|:------:|:----:|:---------------:|
-| 177.3438      | 0.0043 | 1    | 11.0844         |
-| 176.8253      | 0.1074 | 25   | 11.0496         |
-
-
-### Framework versions
-
-- PEFT 0.13.2
-- Transformers 4.46.0
-- Pytorch 2.5.0+cu124
-- Datasets 3.0.1
-- Tokenizers 0.20.1