Update README.md

README.md

@@ -9,130 +9,20 @@ model-index:
   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. -->
+# Philosophy LLM
 
-[<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>
+I would've trained this on Phi so I could've called it Phi-losophy if I had thought of that joke before kicking off the run. Oh well.
+It's trained on Mistral instead. That's a Mist opportunity right there.
 
-axolotl version: `0.4.1`
-```yaml
-# This is an axolotl config that allowed creation of a model knowledgeable about hawaii.
-# Replace the dataset paths under `datasets:` with your own
-# If you want a reference point of what kind of data was fed into this model, check out hawaiitoolkit https://github.com/e-p-armstrong/hawaiitoolkit.git
+This is a narrow domain-expert LLM trained on the top 5 books on Gutenberg:
 
-# Rent a GPU with a compute provider like Vast.ai or Runpod
-# (Make sure it is using the axolotl docker image --- winglian/axolotl:main-latest)
-# Copy this file over to the rented instance, in the /workspace/axolotl directory
-# If running on a single-GPU setup, you must run:
-# conda install -c conda-forge mpi4py mpich
-# Then run this command from the /workspace/axolotl directory:
-# accelerate launch --use_deepspeed -m axolotl.cli.train axolotl_config_hawaii_llama3_Jun_9_2024.yaml
+- The Problems of Philosophy (Bertrand Russell)
+- Beyond Good and Evil (Nietzsche)
+- Thus Spake Zarathustra: A Book for All and None (Nietzsche)
+- The Prince (Machiavelli)
+- Second Treatise of Government
 
-# If using GaLore, do not use deepspeed
-
-# (to copy files over to a rented GPU instance, you'll have to use SSH to Secure CoPy files over from your machine to the rented one. This is what such a command might look like, adapt it to your needs)
-# scp -P 40001 -r ./ root@173.231.62.170:/workspace/axolotl/
-
-
-# TODO to properly make this great, MAKE VARIED SYSTEM PROMPTS FOR ALL THINGS IN THE hawaii DATASET.
-# And make automated code to produce it so that I built it for this project and not the other one.
-# OK, now I am truly back to working on the efficiency problem.
-
-base_model: Heralax/philosophy-llm-mistral-pretrain
-tokenizer_type: AutoTokenizer
-is_mistral_derived_model: true
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: json
-    data_files: philosophy_qa_normal.jsonl
-    ds_type: json
-    type: sharegpt
-    conversation: chatml
-  - path: json
-    data_files: philosophy_qa_open-ended.jsonl
-    ds_type: json
-    type: sharegpt
-    conversation: chatml
-  - path: json
-    data_files: philosophy_qa_negative.jsonl
-    ds_type: json
-    type: sharegpt
-    conversation: chatml
-
-dataset_prepared_path: last_run_prepared
-output_dir: ./philosophy-hardcore-pretraining
-
-sequence_len: 4096
-sample_packing: false
-pad_to_sequence_len: true
-shuffle_merged_datasets: true
-
-wandb_project: mistral-philosophy
-wandb_entity:
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-
-gradient_accumulation_steps: 6
-micro_batch_size: 2
-eval_batch_size: 1
-num_epochs: 6
-optimizer: paged_adamw_8bit
-lr_scheduler: cosine
-learning_rate: 0.000020
-weight_decay: 0
-# Gradient clipping max norm
-max_grad_norm: 1.0
-noisy_embedding_alpha: 0
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16: false
-tf32: false
-
-gradient_checkpointing: unsloth
-early_stopping_patience:
-resume_from_checkpoint: 
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-chat_template: chatml
-
-warmup_ratio: 0.5
-auto_resume_from_checkpoints: false
-#warmup_ratio: 0.5
-eval_steps: 10
-saves_per_epoch: 1
-eval_sample_packing: false
-save_total_limit: 3
-debug:
-deepspeed: deepspeed_configs/zero2.json
-special_tokens:
-  pad_token: "<|end_of_text|>"
-```
-
-</details><br>
-
-# philosophy-hardcore-pretraining
-
-This model is a fine-tuned version of [Heralax/philosophy-llm-mistral-pretrain](https://huggingface.co/Heralax/philosophy-llm-mistral-pretrain) on the None dataset.
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
+It's meant to be an interesting novelty, showing off training on a specific domain. I also forgot to include any generalist assistant data so it's not likely to be good at much else besides answering philosophy questions.
 
 ## Training procedure
 
@@ -153,10 +43,6 @@ The following hyperparameters were used during training:
 - lr_scheduler_warmup_steps: 136
 - num_epochs: 6
 
-### Training results
-
-
-
 ### Framework versions
 
 - Transformers 4.45.0.dev0