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config.json

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+{
+  "_name_or_path": "./6B_code_outputs/checkpoint-10000",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 7168,
+  "max_position_embeddings": 4096,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 8,
+  "pad_token_id": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.2",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 2,
+  "transformers_version": "4.38.2"
+}

mistral_pretrain.py

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+import torch
+from transformers import TrainingArguments, MistralForCausalLM, MistralModel, MistralConfig, AutoTokenizer
+from datasets import load_dataset
+from trl import SFTTrainer
+
+configuration = MistralConfig(vocab_size=32000,
+        hidden_size=2048,
+        intermediate_size=7168,
+        num_hidden_layers=24,
+        num_attention_heads=32,
+        num_key_value_heads=8,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        pad_token_id=2,
+        bos_token_id=1,
+        eos_token_id=2)
+
+#model = MistralForCausalLM(configuration)
+model = MistralForCausalLM.from_pretrained("./6B_code_outputs/checkpoint-10000")
+tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2", local_files_only=False)
+tokenizer.pad_token = tokenizer.eos_token
+
+dataset = load_dataset('HuggingFaceTB/cosmopedia-20k', split="train")
+#dataset = load_dataset('Elriggs/openwebtext-100k', split="train")
+dataset = dataset.shuffle(seed=42)
+print(f'Number of prompts: {len(dataset)}')
+print(f'Column names are: {dataset.column_names}')
+
+def create_prompt_formats(sample):
+    """
+    Format various fields of the sample ('instruction', 'context', 'response')
+    Then concatenate them using two newline characters
+    :param sample: Sample dictionnary
+    """
+    output_texts = []
+    for i in range(len(sample['text'])):
+      formatted_prompt = sample['text'][i]
+      output_texts.append(formatted_prompt)
+    #print(output_texts)
+    return output_texts
+
+
+trainer = SFTTrainer(
+    model,
+    train_dataset=dataset,
+    tokenizer = tokenizer,
+    max_seq_length=2048,
+    formatting_func=create_prompt_formats,
+    args=TrainingArguments(
+            per_device_train_batch_size=2,
+            gradient_accumulation_steps=1,
+            warmup_steps=2,
+            max_steps=10000,
+            learning_rate=1e-4,
+            logging_steps=1,
+            output_dir="6B_outputs", overwrite_output_dir=True,save_steps=1000,
+            optim="paged_adamw_32bit",report_to="none"
+        )
+)
+trainer.train()
+trainer.model.save_pretrained("6B-final", dtype=torch.float32)
+trainer.tokenizer.save_pretrained("6B-final")

model-00001-of-00002.safetensors

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model-00002-of-00002.safetensors

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model.safetensors.index.json

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+}

special_tokens_map.json

@@ -0,0 +1,24 @@
+{
+  "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": "</s>",
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,43 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "bos_token": "<s>",
+  "chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "</s>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}