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Model: Heralax/philosophy-mistral
Source: Original Platform

.gitattributes

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+*.gguf filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text

Philosophy-Llm-Mistral-Pretrain-7.2B-F16.gguf

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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

added_tokens.json

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+{
+  "<|end_of_text|>": 32000
+}

config.json

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+{
+  "_name_or_path": "Heralax/philosophy-llm-mistral-pretrain",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.45.0.dev0",
+  "use_cache": false,
+  "vocab_size": 32001
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "do_sample": true,
+  "eos_token_id": 2,
+  "transformers_version": "4.45.0.dev0"
+}

ggml-model-Q8_0.gguf

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pytorch_model.bin

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special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|end_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "32000": {
+      "content": "<|end_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<|end_of_text|>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}