Upload folder using huggingface_hub
Browse files- config.json +29 -0
- convert_to_safetensors.py +68 -0
- convert_weights.py +84 -0
- generation_config.json +7 -0
- model-00001-of-00005.safetensors +3 -0
- model-00002-of-00005.safetensors +3 -0
- model-00003-of-00005.safetensors +3 -0
- model-00004-of-00005.safetensors +3 -0
- model-00005-of-00005.safetensors +3 -0
- model.safetensors.index.json +370 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +37 -0
- tokenizer.json +0 -0
- tokenizer_config.json +141 -0
config.json
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{
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"_name_or_path": "/scratch/project_465000144/dasamuel/normistral/normistral-11b-masked-post-hf-60000",
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"architectures": [
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"MistralForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 5120,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"mask_token_id": 4,
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"max_position_embeddings": 1024000,
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"model_type": "mistral",
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"num_attention_heads": 32,
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"num_hidden_layers": 40,
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"num_key_value_heads": 8,
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"pad_token_id": 3,
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"rms_norm_eps": 1e-05,
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"rope_theta": 1000000.0,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.44.0.dev0",
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"use_cache": true,
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"vocab_size": 51200
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}
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convert_to_safetensors.py
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import argparse
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import random
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from statistics import mean, stdev
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from typing import List
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import torch
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import torchmetrics
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from datasets import load_dataset
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from tqdm import tqdm
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from transformers import AutoTokenizer, AutoModelForCausalLM
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model_name_or_path",
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type=str,
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default="/scratch/project_465000144/dasamuel/normistral/normistral-11b-masked-post-hf-60000",
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help="Path to the pre-trained model",
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)
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args = parser.parse_args()
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return args
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def load_model(model_path: str):
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# Load the pre-trained model and tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path, cache_dir=".", token="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", torch_dtype=torch.bfloat16)
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model = AutoModelForCausalLM.from_pretrained(model_path, cache_dir=".", token="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", torch_dtype=torch.bfloat16).cuda().eval()
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eos_token_ids = [
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token_id
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for token_id in range(tokenizer.vocab_size)
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if "\n" in tokenizer.decode([token_id])
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]
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if hasattr(model.config, "n_positions"):
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max_length = model.config.n_positions
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elif hasattr(model.config, "max_position_embeddings"):
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max_length = model.config.max_position_embeddings
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elif hasattr(model.config, "max_length"):
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max_length = model.config.max_length
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elif hasattr(model.config, "n_ctx"):
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max_length = model.config.n_ctx
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else:
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max_length = 4096 # Default value
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return {
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"name": model_path.split("/")[-1],
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"tokenizer": tokenizer,
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"model": model,
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"eos_token_ids": eos_token_ids,
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"max_length": max_length,
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}
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def main():
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args = parse_args()
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model = load_model(args.model_name_or_path)
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model["model"].save_pretrained(
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args.model_name_or_path,
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max_shard_size="4.7GB"
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)
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if __name__ == "__main__":
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main()
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convert_weights.py
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import torch
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from safetensors.torch import save_file
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from tqdm import tqdm
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input_dir_path = "/scratch/project_465000144/dasamuel/normistral/normistral-11b-masked-post/global_step10000"
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output_dir_path = "/scratch/project_465000144/dasamuel/normistral/normistral-11b-masked-post-hf-60000"
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n_hidden = 5120
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n_ffn_hidden = 14336
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n_heads = 32
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n_kv_heads = 8
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n_layers = 40
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head_dim = 128
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n_tp = 2
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n_pp = 2
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weights = {}
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# embedding
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embedding_weights = []
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for i in range(n_tp):
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path = f"{input_dir_path}/layer_01-model_0{i}-model_states.pt"
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checkpoint = torch.load(path)
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embedding_weights.append(checkpoint["word_embeddings.weight"].bfloat16())
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weights[f"model.embed_tokens.weight"] = torch.cat(embedding_weights, dim=0)
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del embedding_weights
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lm_head_weights = []
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for i in range(n_tp):
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path = f"{input_dir_path}/layer_{n_layers + 5}-model_0{i}-model_states.pt"
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checkpoint = torch.load(path)
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lm_head_weights.append(checkpoint["lm_head.weight"].bfloat16())
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weights[f"lm_head.weight"] = torch.cat(lm_head_weights, dim=0)
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del lm_head_weights
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# transformer layers
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for layer in tqdm(range(n_layers)):
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q_weights, k_weights, v_weights, o_weights = [], [], [], []
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up_weights, gate_weights, down_weights = [], [], []
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for i in range(n_tp):
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path = f"{input_dir_path}/layer_{layer+3:02d}-model_0{i}-model_states.pt"
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checkpoint = torch.load(path)
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weights[f"model.layers.{layer}.input_layernorm.weight"] = checkpoint["input_layernorm.weight"].bfloat16()
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weights[f"model.layers.{layer}.post_attention_layernorm.weight"] = checkpoint["post_attention_layernorm.weight"].bfloat16()
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kv_weight = checkpoint["self_attention.key_value.weight"].bfloat16()
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k_weight, v_weight = torch.chunk(kv_weight, 2, dim=0)
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k_weights.append(k_weight)
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v_weights.append(v_weight)
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q_weights.append(checkpoint["self_attention.query.weight"].bfloat16())
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o_weights.append(checkpoint["self_attention.dense.weight"].bfloat16())
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down_weights.append(checkpoint["mlp.dense_4h_to_h.weight"].bfloat16())
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up_gate_weight = checkpoint["mlp.dense_h_to_4h.weight"].bfloat16()
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up_weight, gate_weight = torch.chunk(up_gate_weight, 2, dim=0)
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up_weights.append(up_weight)
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gate_weights.append(gate_weight)
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weights[f"model.layers.{layer}.self_attn.q_proj.weight"] = torch.cat(q_weights, dim=0)
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weights[f"model.layers.{layer}.self_attn.k_proj.weight"] = torch.cat(k_weights, dim=0)
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weights[f"model.layers.{layer}.self_attn.v_proj.weight"] = torch.cat(v_weights, dim=0)
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weights[f"model.layers.{layer}.self_attn.o_proj.weight"] = torch.cat(o_weights, dim=1)
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weights[f"model.layers.{layer}.mlp.up_proj.weight"] = torch.cat(up_weights, dim=0)
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weights[f"model.layers.{layer}.mlp.gate_proj.weight"] = torch.cat(gate_weights, dim=0)
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weights[f"model.layers.{layer}.mlp.down_proj.weight"] = torch.cat(down_weights, dim=1)
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# output layer norm
|
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path = f"{input_dir_path}/layer_{n_layers + 4}-model_00-model_states.pt"
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checkpoint = torch.load(path)
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|
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weights[f"model.norm.weight"] = checkpoint["weight"].bfloat16()
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torch.save(weights, f"{output_dir_path}/pytorch_model.bin")
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save_file(weights, f"{output_dir_path}/model.safetensors")
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"pad_token_id": 3,
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"transformers_version": "4.44.0.dev0"
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}
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model-00001-of-00005.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:79d862929732b7da64ec5534edb645e78ff2a0167d83c58adcaa5118f34681a0
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size 4592914240
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model-00002-of-00005.safetensors
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:10c22306066569ad10b7d8e297549718d0db2a7f389408ed3aefc3e73c94c60c
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size 4697813760
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model-00003-of-00005.safetensors
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:5f82cbf4908b09deb347b32fffdf5b78b190d3c5c603e5382a68e5f2c823a449
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size 4571964072
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model-00004-of-00005.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:0d77907f160828e57b447731c6a6ffbfc5a8dc7431b84b408bf5efdb1ef67410
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size 4697813784
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model-00005-of-00005.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:5e6e0d221c5caffecf1021949a4d1f50beecba435c151f6856d0da8f3516e0d3
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size 4299322648
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model.safetensors.index.json
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version https://git-lfs.github.com/spec/v1
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size 22859907189
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special_tokens_map.json
ADDED
@@ -0,0 +1,37 @@
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tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
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tokenizer_config.json
ADDED
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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