convert.py

from transformers import AutoModelForCausalLM
import torch
from safetensors.torch import save_file

model = AutoModelForCausalLM.from_pretrained("Josephgflowers/Phi-3-mini-4k-instruct-Cinder-with-16bit-GGUF", trust_remote_code=True)

params = model.state_dict()
params2 = {}

for r in params.keys():
    if "gate_up_proj" in r:
        (gate, up) = params[r].chunk(2)
        params2[r.replace("gate_up_proj", "gate_proj")] = gate
        params2[r.replace("gate_up_proj", "up_proj")] = up
    elif "qkv_proj" in r:
        (q, k, v) = params[r].chunk(3)
        params2[r.replace("qkv_proj", "q_proj")] = q
        params2[r.replace("qkv_proj", "k_proj")] = k
        params2[r.replace("qkv_proj", "v_proj")] = v
    else:
        params2[r] = params[r]

for r in params2.keys():
    params2[r] = torch.tensor(params2[r].clone().detach(), dtype=torch.bfloat16)

save_file(params2, "model-00001-of-00001.safetensors", metadata={"format": "pt"})