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config.json

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+{
+  "_name_or_path": "Salesforce/codet5-base",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "bos_token_id": 1,
+  "classifier_dropout": 0.0,
+  "d_ff": 3072,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "relu",
+  "dropout_rate": 0.1,
+  "eos_token_id": 2,
+  "feed_forward_proj": "relu",
+  "gradient_checkpointing": false,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": false,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.33.3",
+  "use_cache": true,
+  "vocab_size": 32100
+}

convert_to_safetensor.py

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+import json
+import os
+import shutil
+import torch
+from collections import defaultdict
+from safetensors.torch import load_file, save_file
+from tqdm import tqdm
+
+def shared_pointers(tensors):
+    ptrs = defaultdict(list)
+    for k, v in tensors.items():
+        ptrs[v.data_ptr()].append(k)
+    return [names for names in ptrs.values() if len(names) > 1]
+
+def check_file_size(sf_filename, pt_filename):
+    sf_size = os.stat(sf_filename).st_size
+    pt_size = os.stat(pt_filename).st_size
+    if (sf_size - pt_size) / pt_size > 0.01:
+        raise RuntimeError(f"File size difference exceeds 1% between {sf_filename} and {pt_filename}")
+
+def convert_file(pt_filename, sf_filename, copy_add_data=True):
+    source_folder = os.path.dirname(pt_filename)
+    dest_folder = os.path.dirname(sf_filename)
+    loaded = torch.load(pt_filename, map_location="cpu")
+    loaded = loaded.get("state_dict", loaded)
+    shared = shared_pointers(loaded)
+
+    for shared_weights in shared:
+        for name in shared_weights[1:]:
+            loaded.pop(name)
+
+    loaded = {k: v.contiguous().half() for k, v in loaded.items()}
+
+    os.makedirs(dest_folder, exist_ok=True)
+    save_file(loaded, sf_filename, metadata={"format": "pt"})
+    check_file_size(sf_filename, pt_filename)
+    if copy_add_data:
+        copy_additional_files(source_folder, dest_folder)
+
+    reloaded = load_file(sf_filename)
+    for k, v in loaded.items():
+        if not torch.equal(v, reloaded[k]):
+            raise RuntimeError(f"Mismatch in tensors for key {k}.")
+
+def rename(pt_filename):
+    return pt_filename.replace("pytorch_model", "model").replace(".bin", ".safetensors")
+
+def copy_additional_files(source_folder, dest_folder):
+    for file in os.listdir(source_folder):
+        file_path = os.path.join(source_folder, file)
+        if os.path.isfile(file_path) and not (file.endswith('.bin') or file.endswith('.py')):
+            shutil.copy(file_path, dest_folder)
+
+def find_index_file(source_folder):
+    for file in os.listdir(source_folder):
+        if file.endswith('.bin.index.json'):
+            return file
+    return None
+
+def convert_files(source_folder, dest_folder, delete_old):
+    index_file = find_index_file(source_folder)
+    if not index_file:
+        raise RuntimeError("Index file not found. Please ensure the correct folder is specified.")
+
+    index_file = os.path.join(source_folder, index_file)
+    with open(index_file) as f:
+        index_data = json.load(f)
+
+    for pt_filename in tqdm(set(index_data["weight_map"].values())):
+        full_pt_filename = os.path.join(source_folder, pt_filename)
+        sf_filename = os.path.join(dest_folder, rename(pt_filename))
+        convert_file(full_pt_filename, sf_filename, copy_add_data=False)
+        if delete_old:
+            os.remove(full_pt_filename)
+
+    copy_additional_files(source_folder, dest_folder)
+    
+    index_path = os.path.join(dest_folder, "model.safetensors.index.json")
+    with open(index_path, "w") as f:
+        new_map = {k: rename(v) for k, v in index_data["weight_map"].items()}
+        json.dump({**index_data, "weight_map": new_map}, f, indent=4)
+
+def main():
+    script_dir = os.path.dirname(os.path.realpath(__file__))
+
+    source_folder = input("Source folder for PyTorch files (leave blank for script's directory): ").strip() or script_dir
+    dest_folder = input("Destination folder for SafeTensors files (leave blank for default): ").strip()
+
+    if not dest_folder:
+        model_name = os.path.basename(os.path.normpath(source_folder))
+        dest_folder = os.path.join(source_folder, model_name + "_safetensors")
+
+    delete_old = input("Delete old PyTorch files? (Y/N): ").strip().upper() == 'Y'
+
+    if "pytorch_model.bin" in os.listdir(source_folder):
+        convert_file(os.path.join(source_folder, "pytorch_model.bin"), os.path.join(dest_folder, "model.safetensors"), copy_add_data=True)
+        if delete_old:
+            os.remove(os.path.join(source_folder, "pytorch_model.bin"))
+    else:
+        convert_files(source_folder, dest_folder, delete_old)
+
+if __name__ == "__main__":
+    main()

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "decoder_start_token_id": 0,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.33.3"
+}