init

config.json

@@ -0,0 +1,23 @@
+{
+  "_name_or_path": "Austism/chronos-hermes-13b",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 13824,
+  "max_position_embeddings": 2048,
+  "model_type": "llama",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-06,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.30.2",
+  "use_cache": true,
+  "vocab_size": 32001
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.30.2"
+}

merge.py

@@ -0,0 +1,56 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+import logging
+import argparse
+
+logger = logging.getLogger(__name__)
+
+def merge_lora(base_model_name_or_path, peft_model_path, output_dir, device='auto', push_to_hub=False):
+    if device == 'auto':
+        device_arg = { 'device_map': 'auto' }
+    else:
+        device_arg = { 'device_map': { "": device} }
+
+    logger.info(f"Loading base model: {base_model_name_or_path}")
+    base_model = AutoModelForCausalLM.from_pretrained(
+        base_model_name_or_path,
+        return_dict=True,
+        torch_dtype=torch.float16,
+        **device_arg
+    )
+
+    logger.info(f"Loading PEFT: {peft_model_path}")
+    model = PeftModel.from_pretrained(base_model, peft_model_path, torch_dtype=torch.float16, **device_arg)
+
+    logger.info(f"Running merge_and_unload")
+    model = model.merge_and_unload()
+
+    tokenizer = AutoTokenizer.from_pretrained(base_model_name_or_path)
+
+    if push_to_hub:
+        logger.info(f"Saving to hub ...")
+        model.push_to_hub(f"{output_dir}", use_temp_dir=False)
+        tokenizer.push_to_hub(f"{output_dir}", use_temp_dir=False)
+    else:
+        model.save_pretrained(output_dir)
+        tokenizer.save_pretrained(output_dir, torch_dtype=torch.float16)
+        logger.info(f"Model saved to {output_dir}")
+
+if __name__ == "__main__" :
+    logger = logging.getLogger()
+    logging.basicConfig(
+        format="%(asctime)s %(levelname)s [%(name)s] %(message)s", level=logging.INFO, datefmt="%Y-%m-%d %H:%M:%S"
+    )
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--base_model_name_or_path", type=str)
+    parser.add_argument("--peft_model_path", type=str)
+    parser.add_argument("--output_dir", type=str)
+    parser.add_argument("--device", type=str, default="auto")
+    parser.add_argument("--push_to_hub", action="store_true")
+
+    args = parser.parse_args()
+
+    merge_lora(base_model_name_or_path = args.base_model_name_or_path, peft_model_path = args.peft_model_path,
+               output_dir = args.output_dir, device = args.device, push_to_hub = args.push_to_hub)

onstart.sh

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+echo 'starting up'

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