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EXAONE AI Model License Agreement 1.1 - NC
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EXAONE AI Model. The Licensor holds all rights, title, and interest in the Model and is responsible for
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---
license: other
license_name: exaone
license_link: LICENSE
language:
- en
- ko
tags:
- lg-ai
- exaone
---
# maywell/EXAONE-3.0-7.8B-Instruct-Llamafied
## Llamafy Script
```python
import torch
import gc
from transformers import LlamaConfig, LlamaForCausalLM, AutoModelForCausalLM, AutoTokenizer
from tqdm import tqdm
def unload_model(model):
"""Clear memory by deleting a model and calling the garbage collector."""
del model
gc.collect()
if torch.cuda.is_available():
torch.cuda.empty_cache()
def create_llama_config(exaone_config):
"""Create and return a Llama configuration based on EXAONE config."""
return LlamaConfig(
vocab_size=exaone_config.vocab_size,
hidden_size=exaone_config.hidden_size,
intermediate_size=exaone_config.intermediate_size,
num_hidden_layers=exaone_config.num_layers,
num_attention_heads=exaone_config.num_attention_heads,
max_position_embeddings=exaone_config.max_position_embeddings,
rms_norm_eps=exaone_config.layer_norm_epsilon,
num_key_value_heads=exaone_config.num_key_value_heads,
rope_theta=exaone_config.rope_theta,
bos_token_id=exaone_config.bos_token_id,
eos_token_id=exaone_config.eos_token_id,
pad_token_id=exaone_config.pad_token_id,
attention_bias=False,
)
def copy_embedding_weights(llama_model, exaone_model):
"""Copy embedding weights from EXAONE to Llama model."""
llama_model.model.embed_tokens.weight.data = exaone_model.transformer.wte.weight.data.to(llama_model.device)
def copy_layer_weights(llama_layer, exaone_layer, device):
"""Copy weights for a single layer from EXAONE to Llama model."""
# Self-attention
llama_layer.self_attn.q_proj.weight.data = exaone_layer.attn.attention.q_proj.weight.data.to(device)
llama_layer.self_attn.k_proj.weight.data = exaone_layer.attn.attention.k_proj.weight.data.to(device)
llama_layer.self_attn.v_proj.weight.data = exaone_layer.attn.attention.v_proj.weight.data.to(device)
llama_layer.self_attn.o_proj.weight.data = exaone_layer.attn.attention.out_proj.weight.data.to(device)
# MLP
llama_layer.mlp.gate_proj.weight.data = exaone_layer.mlp.c_fc_0.weight.data.to(device)
llama_layer.mlp.up_proj.weight.data = exaone_layer.mlp.c_fc_1.weight.data.to(device)
llama_layer.mlp.down_proj.weight.data = exaone_layer.mlp.c_proj.weight.data.to(device)
# Layer Norms
llama_layer.input_layernorm.weight.data = exaone_layer.ln_1.weight.data.to(device)
llama_layer.post_attention_layernorm.weight.data = exaone_layer.ln_2.weight.data.to(device)
def copy_final_weights(llama_model, exaone_model):
"""Copy final layer norm and LM head weights from EXAONE to Llama model."""
llama_model.model.norm.weight.data = exaone_model.transformer.ln_f.weight.data.to(llama_model.device)
llama_model.lm_head.weight.data = exaone_model.lm_head.weight.data.to(llama_model.device)
def port_exaone_to_llama(exaone_model_path, llama_model_path):
print("Loading EXAONE model and tokenizer...")
exaone_model = AutoModelForCausalLM.from_pretrained(exaone_model_path, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)
exaone_tokenizer = AutoTokenizer.from_pretrained(exaone_model_path, trust_remote_code=True)
exaone_config = exaone_model.config
print("Creating Llama configuration...")
llama_config = create_llama_config(exaone_config)
print("Initializing Llama model...")
llama_model = LlamaForCausalLM(llama_config)
llama_model.to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))
print("Copying weights...")
copy_embedding_weights(llama_model, exaone_model)
for i in tqdm(range(exaone_config.num_layers), desc="Copying layers"):
copy_layer_weights(llama_model.model.layers[i], exaone_model.transformer.h[i], llama_model.device)
copy_final_weights(llama_model, exaone_model)
print("Unloading EXAONE model to free memory...")
unload_model(exaone_model)
print(f"Saving ported Llama model and tokenizer to {llama_model_path}")
llama_model.save_pretrained(llama_model_path, safe_serialization=True, max_shard_size="5GB")
exaone_tokenizer.save_pretrained(llama_model_path)
print("Unloading Llama model...")
unload_model(llama_model)
print(f"EXAONE model successfully ported to Llama format and saved at {llama_model_path}")
if __name__ == "__main__":
exaone_model_path = "LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct"
llama_model_path = "./exa_llamafied"
port_exaone_to_llama(exaone_model_path, llama_model_path)
```
### Thanks to
> 코드 업데이트와 모델 업로드를 해주신 [@kuotient](https://huggingface.co/kuotient)
> 모델을 공개해주신 `LG AI Research` - [Original Repository](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)

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{
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 1,
"eos_token_id": 361,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 4096,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 0,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": null,
"rope_theta": 500000.0,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.44.0",
"use_cache": true,
"vocab_size": 102400
}

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{
"_from_model_config": true,
"bos_token_id": 1,
"eos_token_id": 361,
"pad_token_id": 0,
"transformers_version": "4.44.0"
}

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{
"bos_token": {
"content": "[BOS]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"eos_token": {
"content": "[|endofturn|]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"pad_token": {
"content": "[PAD]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"unk_token": {
"content": "[UNK]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}

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