Update README.md

README.md

@@ -1,136 +1,15 @@
 ---
-base_model: Undi95/Meta-Llama-3-8B-hf
-library_name: peft
-license: other
-tags:
-- generated_from_trainer
-model-index:
-- name: outputs/qlora-out
-  results: []
+license: llama3
 ---
+![download.png](https://raw.githubusercontent.com/Fischherboot/Aculi/main/watermark-no-bg.png)
 
-<!-- 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. -->
+# Llama3-8B-Script-Writer (v1)
 
-[<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>
+This Model is based on [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
 
-axolotl version: `0.4.1`
-```yaml
-base_model: Undi95/Meta-Llama-3-8B-hf
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
+It was a Request from my friend, no idea if its good.
 
-load_in_8bit: false
-load_in_4bit: true
-strict: false
+Model will get uplaoded soon.
 
-datasets:
-  - path: Fischerboot/freedom-rp-alpaca-shortend
-    type: alpaca
-  - path: Fischerboot/yannik-data
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0
-output_dir: ./outputs/qlora-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 4096
-sample_packing: true
-pad_to_sequence_len: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 4
-optimizer: paged_adamw_32bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-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_steps: 10
-evals_per_epoch: 2
-eval_table_size:
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  pad_token: "<|end_of_text|>"
-
-```
-
-</details><br>
-
-# outputs/qlora-out
-
-This model is a fine-tuned version of [Undi95/Meta-Llama-3-8B-hf](https://huggingface.co/Undi95/Meta-Llama-3-8B-hf) on the None 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: 4
-- total_train_batch_size: 8
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_steps: 10
-- num_epochs: 4
-
-### Training results
-
-
-
-### Framework versions
-
-- PEFT 0.11.1
-- Transformers 4.42.3
-- Pytorch 2.1.2+cu118
-- Datasets 2.19.1
-- Tokenizers 0.19.1
+### Have fun
+### <u>This is NOT an instruct Model!</u>