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Model: Heralax/philosophy-mistral
Source: Original Platform

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: Heralax/philosophy-llm-mistral-pretrain
+tags:
+- generated_from_trainer
+model-index:
+- name: philosophy-hardcore-pretraining
+  results: []
+---
+
+# Philosophy LLM
+
+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.
+
+This is a narrow domain-expert LLM trained on the top 5 books on Gutenberg:
+
+- 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
+
+It's meant to be an interesting novelty, showing off training on a specific domain. It has some quirks. Namely:
+
+1. It seems to have memorized the training data very well. Ask a question that exists in the training data, with temp 0, and it will usually give you back the exact response word-for-word. This means that, on the subjects covered by its data, it will be very knowledgeable.
+2. I forgot to include any generalist instruct data, so it's... not stupid, at least not particularly stupid by 7b standards, but it is very much limited to QA.
+3. It's much less fluffy and wasteful with its responses than previous Augmentoolkit domain expert models, due to using a new dataset setting. This tends to make it respond with less detail, but it also may remember stuff better and get to the point easier.
+
+Some example chats (blame LM studio for not hiding the stop token):
+
+Asking stuff from the training data:
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64825ebceb4befee377cf8ac/AirHFo61iB1HAP-IXwnZn.png)
+
+Asking a question directly from the training data and one I came up with on the spot.
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64825ebceb4befee377cf8ac/Ccm-EeDyOFcylCefwDS-W.png)
+
+Some things that are kinda funny but also show off the drawback of not using any generalist data:
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64825ebceb4befee377cf8ac/e2sCBLIX8Xg91KSevGt_B.png))
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64825ebceb4befee377cf8ac/P0bhWyENxOaxPvC4fE6jw.png)
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 2
+- eval_batch_size: 1
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 6
+- gradient_accumulation_steps: 6
+- total_train_batch_size: 72
+- total_eval_batch_size: 6
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 136
+- num_epochs: 6
+
+### Framework versions
+
+- Transformers 4.45.0.dev0
+- Pytorch 2.3.1+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1