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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: Heralax/philosophy-llm-mistral-pretrain |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: philosophy-hardcore-pretraining |
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results: [] |
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--- |
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[](https://hf.co/QuantFactory) |
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# QuantFactory/philosophy-mistral-GGUF |
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This is quantized version of [Heralax/philosophy-mistral](https://huggingface.co/Heralax/philosophy-mistral) created using llama.cpp |
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# Original Model Card |
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# Philosophy LLM |
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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. |
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It's trained on Mistral instead. That's a Mist opportunity right there. |
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This is a narrow domain-expert LLM trained on the top 5 books on Gutenberg: |
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- The Problems of Philosophy (Bertrand Russell) |
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- Beyond Good and Evil (Nietzsche) |
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- Thus Spake Zarathustra: A Book for All and None (Nietzsche) |
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- The Prince (Machiavelli) |
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- Second Treatise of Government |
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It's meant to be an interesting novelty, showing off training on a specific domain. It has some quirks. Namely: |
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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. |
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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. |
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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. |
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Some example chats (blame LM studio for not hiding the stop token): |
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Asking stuff from the training data: |
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Asking a question directly from the training data and one I came up with on the spot. |
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Some things that are kinda funny but also show off the drawback of not using any generalist data: |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 6 |
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- gradient_accumulation_steps: 6 |
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- total_train_batch_size: 72 |
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- total_eval_batch_size: 6 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 136 |
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- num_epochs: 6 |
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### Framework versions |
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- Transformers 4.45.0.dev0 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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