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Initialize Deeplm model with config and BitNet quantization, save to safetensors.
"""
import sys
import os
import json
import torch
# Add deeplm to path
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "deeplm"))
from deeplm.config import DeeplmConfig
from deeplm.model.deeplm import DeeplmModel
from deeplm.quantization.bitnet_quantize import apply_bitnet_quantization
def main():
print("Building DeeplmConfig...")
config = DeeplmConfig(
vocab_size=32000,
max_seq_length=4096,
dtype="float32",
)
config.architecture.num_layers = 10
config.architecture.hidden_size = 512
config.architecture.intermediate_size = 2048
config.architecture.num_attention_heads = 8
config.architecture.num_key_value_heads = 1
config.architecture.head_dim = 128
config.architecture.rope_head_dim = 64
config.architecture.nope_head_dim = 64
config.architecture.max_seq_length = 4096
config.architecture.rope_theta = 50000.0
config.mla.q_lora_rank = 192
config.mla.kv_lora_rank = 64
config.mla.qk_rope_head_dim = 64
config.mla.qk_nope_head_dim = 64
config.mla.v_head_dim = 128
config.mla.num_heads = 8
config.mla.kv_heads = 1
config.moe.num_routed_experts = 4
config.moe.num_shared_experts = 1
config.moe.top_k = 2
config.mtp.num_mtp_layers = 2
config.mtp.mtp_depth = 2
config.mtp.mtp_hidden_size = 512
config.output_heads.lm_head.type = "tied"
config.output_heads.lm_head.bias = False
print(f"Creating DeeplmModel...")
model = DeeplmModel(config)
total_params = model.num_parameters()
print(f"Total parameters: {total_params:,}")
print("Applying BitNet b1.58 ternary quantization (absmean)...")
stats = apply_bitnet_quantization(model, scale="absmean", verbose=True)
print(f"Quantized {stats['quantized']}/{stats['total_linear']} linear layers")
print("Saving to model.safetensors...")
from safetensors.torch import save_file
state_dict = model.state_dict()
save_file(state_dict, "model.safetensors")
# Save config.json
config_json = {
"architectures": ["DeeplmModel"],
"model_type": "deeplm",
"vocab_size": 32000,
"hidden_size": 512,
"intermediate_size": 2048,
"num_hidden_layers": 10,
"num_attention_heads": 8,
"num_key_value_heads": 1,
"max_position_embeddings": 4096,
"rms_norm_eps": 1e-06,
"rope_theta": 50000.0,
"rope_dim": 64,
"tie_word_embeddings": True,
"num_routed_experts": 4,
"num_shared_experts": 1,
"expert_topk": 2,
"q_lora_rank": 192,
"kv_lora_rank": 64,
"qk_rope_head_dim": 64,
"qk_nope_head_dim": 64,
"v_head_dim": 128,
"mtp_depth": 2,
"mtp_num_layers": 2,
"bitnet_quantized": True,
"bitnet_scale": "absmean",
}
with open("config.json", "w") as f:
json.dump(config_json, f, indent=2)
print("Saved config.json")
# Save generation_config.json
gen_config = {
"max_new_tokens": 512,
"do_sample": True,
"temperature": 0.7,
"top_p": 0.9,
"top_k": 50,
"repetition_penalty": 1.1,
"pad_token_id": 0,
"eos_token_id": 2,
"bos_token_id": 1,
}
with open("generation_config.json", "w") as f:
json.dump(gen_config, f, indent=2)
print("Saved generation_config.json")
print("Done!")
if __name__ == "__main__":
main()
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