modify convert
This commit is contained in:
caitianchi
parent
ce0d1a6f29
commit
6cad864cbd
6 changed files with 15 additions and 444 deletions
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import argparse
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import os
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import json
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import re
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import torch
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import numpy as np
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from gguf import *
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from transformers.models.idefics2.modeling_idefics2 import Idefics2VisionTransformer, Idefics2VisionConfig
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TEXT = "clip.text"
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VISION = "clip.vision"
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def add_key_str(raw_key: str, arch: str) -> str:
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return raw_key.format(arch=arch)
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def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_minicpmv: bool) -> bool:
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if name in (
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"logit_scale",
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"text_model.embeddings.position_ids",
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"vision_model.embeddings.position_ids",
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):
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return True
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if has_minicpmv and name in ["visual_projection.weight"]:
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return True
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if name.startswith("v") and not has_vision:
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return True
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if name.startswith("t") and not has_text:
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return True
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return False
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def get_tensor_name(name: str) -> str:
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if "projection" in name:
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return name
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if "mm_projector" in name:
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name = name.replace("model.mm_projector", "mm")
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name = re.sub(r'mm\.mlp\.mlp', 'mm.model.mlp', name, count=1)
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name = re.sub(r'mm\.peg\.peg', 'mm.model.peg', name, count=1)
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return name
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return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln")
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def bytes_to_unicode():
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"""
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Returns list of utf-8 byte and a corresponding list of unicode strings.
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The reversible bpe codes work on unicode strings.
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This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
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When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
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This is a significant percentage of your normal, say, 32K bpe vocab.
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To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
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And avoids mapping to whitespace/control characters the bpe code barfs on.
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"""
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bs = (
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list(range(ord("!"), ord("~") + 1))
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+ list(range(ord("¡"), ord("¬") + 1))
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+ list(range(ord("®"), ord("ÿ") + 1))
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)
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cs = bs[:]
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n = 0
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for b in range(2**8):
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if b not in bs:
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bs.append(b)
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cs.append(2**8 + n)
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n += 1
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cs = [chr(n) for n in cs]
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return dict(zip(bs, cs))
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ap = argparse.ArgumentParser()
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ap.add_argument("-m", "--model-dir", help="Path to model directory cloned from HF Hub", required=True)
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ap.add_argument("--use-f32", action="store_true", default=False, help="Use f32 instead of f16")
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ap.add_argument("--text-only", action="store_true", required=False,
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help="Save a text-only model. It can't be used to encode images")
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ap.add_argument("--vision-only", action="store_true", required=False,
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help="Save a vision-only model. It can't be used to encode texts")
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ap.add_argument("--clip-model-is-vision", action="store_true", required=False,
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help="The clip model is a pure vision model (ShareGPT4V vision extract for example)")
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ap.add_argument("--clip-model-is-openclip", action="store_true", required=False,
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help="The clip model is from openclip (for ViT-SO400M type))")
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ap.add_argument("--minicpmv-projector", help="Path to minicpmv.projector file. If specified, save an image encoder for MiniCPM-V models.")
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ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2"], default="mlp")
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ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
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# Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711
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# Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5
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default_image_mean = [0.48145466, 0.4578275, 0.40821073]
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default_image_std = [0.26862954, 0.26130258, 0.27577711]
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ap.add_argument('--image-mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
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ap.add_argument('--image-std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)
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# with proper
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args = ap.parse_args()
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if args.text_only and args.vision_only:
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print("--text-only and --image-only arguments cannot be specified at the same time.")
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exit(1)
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if args.use_f32:
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print("WARNING: Weights for the convolution op is always saved in f16, as the convolution op in GGML does not support 32-bit kernel weights yet.")
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# output in the same directory as the model if output_dir is None
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dir_model = args.model_dir
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if args.clip_model_is_vision or not os.path.exists(dir_model + "/vocab.json") or args.clip_model_is_openclip:
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vocab = None
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tokens = None
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else:
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with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
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vocab = json.load(f)
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tokens = [key for key in vocab]
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# possible data types
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# ftype == 0 -> float32
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# ftype == 1 -> float16
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#
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# map from ftype to string
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ftype_str = ["f32", "f16"]
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ftype = 1
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if args.use_f32:
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ftype = 0
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# if args.clip_model_is_vision or args.clip_model_is_openclip:
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# model = CLIPVisionModel.from_pretrained(dir_model)
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# processor = None
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# else:
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# model = CLIPModel.from_pretrained(dir_model)
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# processor = CLIPProcessor.from_pretrained(dir_model)
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default_vision_config = {
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"hidden_size": 1152,
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"image_size": 980,
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"intermediate_size": 4304,
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"model_type": "idefics2",
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"num_attention_heads": 16,
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"num_hidden_layers": 27,
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"patch_size": 14,
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}
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vision_config = Idefics2VisionConfig(**default_vision_config)
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model = Idefics2VisionTransformer(vision_config)
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processor = None
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# if model.attn_pool is not None:
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# model.attn_pool = torch.nn.Identity()
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# model.blocks = model.blocks[:-1]
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model.load_state_dict(torch.load(os.path.join(dir_model, "minicpmv.clip")))
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fname_middle = None
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has_text_encoder = True
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has_vision_encoder = True
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has_minicpmv_projector = False
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if args.text_only:
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fname_middle = "text-"
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has_vision_encoder = False
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elif args.minicpmv_projector is not None:
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fname_middle = "mmproj-"
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has_text_encoder = False
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has_minicpmv_projector = True
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elif args.vision_only:
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fname_middle = "vision-"
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has_text_encoder = False
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else:
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fname_middle = ""
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output_dir = args.output_dir if args.output_dir is not None else dir_model
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os.makedirs(output_dir, exist_ok=True)
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output_prefix = os.path.basename(output_dir).replace("ggml_", "")
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fname_out = os.path.join(output_dir, f"{fname_middle}model-{ftype_str[ftype]}.gguf")
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fout = GGUFWriter(path=fname_out, arch="clip")
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fout.add_bool("clip.has_text_encoder", has_text_encoder)
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fout.add_bool("clip.has_vision_encoder", has_vision_encoder)
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fout.add_bool("clip.has_minicpmv_projector", has_minicpmv_projector)
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fout.add_file_type(ftype)
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if args.text_only:
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fout.add_description("text-only CLIP model")
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elif args.vision_only and not has_minicpmv_projector:
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fout.add_description("vision-only CLIP model")
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elif has_minicpmv_projector:
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fout.add_description("image encoder for MiniCPM-V")
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# add projector type
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fout.add_string("clip.projector_type", "resampler")
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else:
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fout.add_description("two-tower CLIP model")
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if has_vision_encoder:
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# vision_model hparams
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fout.add_uint32("clip.vision.image_size", 448)
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fout.add_uint32("clip.vision.patch_size", 14)
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fout.add_uint32(add_key_str(KEY_EMBEDDING_LENGTH, VISION), 1152)
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fout.add_uint32(add_key_str(KEY_FEED_FORWARD_LENGTH, VISION), 4304)
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fout.add_uint32("clip.vision.projection_dim", 0)
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fout.add_uint32(add_key_str(KEY_ATTENTION_HEAD_COUNT, VISION), 16)
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fout.add_float32(add_key_str(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6)
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block_count = 26
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fout.add_uint32(add_key_str(KEY_BLOCK_COUNT, VISION), block_count)
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if processor is not None:
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image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean
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image_std = processor.image_processor.image_std if args.image_std is None or args.image_std == default_image_std else args.image_std
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else:
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image_mean = args.image_mean if args.image_mean is not None else default_image_mean
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image_std = args.image_std if args.image_std is not None else default_image_std
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fout.add_array("clip.vision.image_mean", image_mean)
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fout.add_array("clip.vision.image_std", image_std)
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use_gelu = True
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fout.add_bool("clip.use_gelu", use_gelu)
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def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
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"""
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embed_dim: output dimension for each position
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pos: a list of positions to be encoded: size (M,)
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out: (M, D)
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"""
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assert embed_dim % 2 == 0
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omega = np.arange(embed_dim // 2, dtype=np.float32)
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omega /= embed_dim / 2.
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omega = 1. / 10000 ** omega # (D/2,)
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pos = pos.reshape(-1) # (M,)
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out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
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emb_sin = np.sin(out) # (M, D/2)
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emb_cos = np.cos(out) # (M, D/2)
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emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
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return emb
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def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
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assert embed_dim % 2 == 0
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# use half of dimensions to encode grid_h
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emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
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emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
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emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
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return emb
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# https://github.com/facebookresearch/mae/blob/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/pos_embed.py#L20
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def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
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"""
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grid_size: int of the grid height and width
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return:
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pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
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"""
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if isinstance(grid_size, int):
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grid_h_size, grid_w_size = grid_size, grid_size
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else:
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grid_h_size, grid_w_size = grid_size[0], grid_size[1]
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grid_h = np.arange(grid_h_size, dtype=np.float32)
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grid_w = np.arange(grid_w_size, dtype=np.float32)
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grid = np.meshgrid(grid_w, grid_h) # here w goes first
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grid = np.stack(grid, axis=0)
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grid = grid.reshape([2, 1, grid_h_size, grid_w_size])
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pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
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if cls_token:
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pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
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return pos_embed
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def _replace_name_resampler(s, v):
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if re.match("resampler.pos_embed", s):
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return {
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s: v,
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re.sub("pos_embed", "pos_embed_k", s): torch.from_numpy(get_2d_sincos_pos_embed(4096, (70, 70))),
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}
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if re.match("resampler.proj", s):
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return {
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re.sub("proj", "pos_embed_k", s): torch.from_numpy(get_2d_sincos_pos_embed(4096, (70, 70))),
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re.sub("proj", "proj.weight", s): v.transpose(-1, -2).contiguous(),
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}
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if re.match("resampler.attn.in_proj_.*", s):
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return {
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re.sub("attn.in_proj_", "attn.q.", s): v.chunk(3, dim=0)[0],
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re.sub("attn.in_proj_", "attn.k.", s): v.chunk(3, dim=0)[1],
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re.sub("attn.in_proj_", "attn.v.", s): v.chunk(3, dim=0)[2],
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}
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return {s: v}
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if has_minicpmv_projector:
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projector = torch.load(args.minicpmv_projector)
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new_state_dict = {}
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for k, v in projector.items():
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kvs = _replace_name_resampler(k, v)
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for nk, nv in kvs.items():
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new_state_dict[nk] = nv
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projector = new_state_dict
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ftype_cur = 0
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for name, data in projector.items():
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name = get_tensor_name(name)
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data = data.squeeze().numpy()
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n_dims = len(data.shape)
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if ftype == 1:
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if name[-7:] == ".weight" and n_dims == 2:
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print(" Converting to float16")
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data = data.astype(np.float16)
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ftype_cur = 1
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else:
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print(" Converting to float32")
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data = data.astype(np.float32)
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ftype_cur = 0
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else:
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if data.dtype != np.float32:
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print(" Converting to float32")
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data = data.astype(np.float32)
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ftype_cur = 0
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fout.add_tensor(name, data)
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print(f"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}")
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print("Projector tensors added\n")
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def _replace_name(s, v):
|
|
||||||
s = "vision_model." + s
|
|
||||||
if re.match("vision_model.embeddings.position_embedding", s):
|
|
||||||
v = v.unsqueeze(0)
|
|
||||||
return {s: v}
|
|
||||||
|
|
||||||
return {s: v}
|
|
||||||
|
|
||||||
state_dict = model.state_dict()
|
|
||||||
new_state_dict = {}
|
|
||||||
for k, v in state_dict.items():
|
|
||||||
kvs = _replace_name(k, v)
|
|
||||||
for nk, nv in kvs.items():
|
|
||||||
new_state_dict[nk] = nv
|
|
||||||
state_dict = new_state_dict
|
|
||||||
for name, data in state_dict.items():
|
|
||||||
if should_skip_tensor(name, has_text_encoder, has_vision_encoder, has_minicpmv_projector):
|
|
||||||
# we don't need this
|
|
||||||
print(f"skipping parameter: {name}")
|
|
||||||
continue
|
|
||||||
|
|
||||||
name = get_tensor_name(name)
|
|
||||||
data = data.squeeze().numpy()
|
|
||||||
|
|
||||||
n_dims = len(data.shape)
|
|
||||||
|
|
||||||
# ftype == 0 -> float32, ftype == 1 -> float16
|
|
||||||
ftype_cur = 0
|
|
||||||
if n_dims == 4:
|
|
||||||
print(f"tensor {name} is always saved in f16")
|
|
||||||
data = data.astype(np.float16)
|
|
||||||
ftype_cur = 1
|
|
||||||
elif ftype == 1:
|
|
||||||
if name[-7:] == ".weight" and n_dims == 2:
|
|
||||||
print(" Converting to float16")
|
|
||||||
data = data.astype(np.float16)
|
|
||||||
ftype_cur = 1
|
|
||||||
else:
|
|
||||||
print(" Converting to float32")
|
|
||||||
data = data.astype(np.float32)
|
|
||||||
ftype_cur = 0
|
|
||||||
else:
|
|
||||||
if data.dtype != np.float32:
|
|
||||||
print(" Converting to float32")
|
|
||||||
data = data.astype(np.float32)
|
|
||||||
ftype_cur = 0
|
|
||||||
|
|
||||||
print(f"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}")
|
|
||||||
fout.add_tensor(name, data)
|
|
||||||
|
|
||||||
|
|
||||||
fout.write_header_to_file()
|
|
||||||
fout.write_kv_data_to_file()
|
|
||||||
fout.write_tensors_to_file()
|
|
||||||
fout.close()
|
|
||||||
|
|
||||||
print("Done. Output file: " + fname_out)
|
|
||||||
|
|
@ -13,7 +13,7 @@ TEXT = "clip.text"
|
||||||
VISION = "clip.vision"
|
VISION = "clip.vision"
|
||||||
|
|
||||||
|
|
||||||
def k(raw_key: str, arch: str) -> str:
|
def add_key_str(raw_key: str, arch: str) -> str:
|
||||||
return raw_key.format(arch=arch)
|
return raw_key.format(arch=arch)
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -199,13 +199,13 @@ if has_vision_encoder:
|
||||||
# vision_model hparams
|
# vision_model hparams
|
||||||
fout.add_uint32("clip.vision.image_size", 448)
|
fout.add_uint32("clip.vision.image_size", 448)
|
||||||
fout.add_uint32("clip.vision.patch_size", 14)
|
fout.add_uint32("clip.vision.patch_size", 14)
|
||||||
fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), 1152)
|
fout.add_uint32(add_key_str(KEY_EMBEDDING_LENGTH, VISION), 1152)
|
||||||
fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), 4304)
|
fout.add_uint32(add_key_str(KEY_FEED_FORWARD_LENGTH, VISION), 4304)
|
||||||
fout.add_uint32("clip.vision.projection_dim", 0)
|
fout.add_uint32("clip.vision.projection_dim", 0)
|
||||||
fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), 16)
|
fout.add_uint32(add_key_str(KEY_ATTENTION_HEAD_COUNT, VISION), 16)
|
||||||
fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6)
|
fout.add_float32(add_key_str(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6)
|
||||||
block_count = 26
|
block_count = 26
|
||||||
fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), block_count)
|
fout.add_uint32(add_key_str(KEY_BLOCK_COUNT, VISION), block_count)
|
||||||
|
|
||||||
if processor is not None:
|
if processor is not None:
|
||||||
image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean
|
image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean
|
||||||
|
|
@ -300,6 +300,7 @@ if has_minicpmv_projector:
|
||||||
for nk, nv in kvs.items():
|
for nk, nv in kvs.items():
|
||||||
new_state_dict[nk] = nv
|
new_state_dict[nk] = nv
|
||||||
projector = new_state_dict
|
projector = new_state_dict
|
||||||
|
ftype_cur = 0
|
||||||
for name, data in projector.items():
|
for name, data in projector.items():
|
||||||
name = get_tensor_name(name)
|
name = get_tensor_name(name)
|
||||||
data = data.squeeze().numpy()
|
data = data.squeeze().numpy()
|
||||||
|
|
|
||||||
|
|
@ -1,5 +1,4 @@
|
||||||
import argparse
|
import argparse
|
||||||
import glob
|
|
||||||
import os
|
import os
|
||||||
import torch
|
import torch
|
||||||
from transformers import AutoModel, AutoTokenizer
|
from transformers import AutoModel, AutoTokenizer
|
||||||
|
|
|
||||||
|
|
@ -20,7 +20,7 @@ import os
|
||||||
import math
|
import math
|
||||||
import warnings
|
import warnings
|
||||||
from dataclasses import dataclass
|
from dataclasses import dataclass
|
||||||
from typing import Any, Optional, Tuple, Union
|
from typing import Optional, Tuple, Union
|
||||||
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import torch
|
import torch
|
||||||
|
|
@ -949,7 +949,7 @@ TEXT = "clip.text"
|
||||||
VISION = "clip.vision"
|
VISION = "clip.vision"
|
||||||
|
|
||||||
|
|
||||||
def k(raw_key: str, arch: str) -> str:
|
def add_key_str(raw_key: str, arch: str) -> str:
|
||||||
return raw_key.format(arch=arch)
|
return raw_key.format(arch=arch)
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -1136,13 +1136,13 @@ if has_vision_encoder:
|
||||||
# vision_model hparams
|
# vision_model hparams
|
||||||
fout.add_uint32("clip.vision.image_size", 448)
|
fout.add_uint32("clip.vision.image_size", 448)
|
||||||
fout.add_uint32("clip.vision.patch_size", 14)
|
fout.add_uint32("clip.vision.patch_size", 14)
|
||||||
fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), 1152)
|
fout.add_uint32(add_key_str(KEY_EMBEDDING_LENGTH, VISION), 1152)
|
||||||
fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), 4304)
|
fout.add_uint32(add_key_str(KEY_FEED_FORWARD_LENGTH, VISION), 4304)
|
||||||
fout.add_uint32("clip.vision.projection_dim", 0)
|
fout.add_uint32("clip.vision.projection_dim", 0)
|
||||||
fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), 16)
|
fout.add_uint32(add_key_str(KEY_ATTENTION_HEAD_COUNT, VISION), 16)
|
||||||
fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6)
|
fout.add_float32(add_key_str(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6)
|
||||||
block_count = 26
|
block_count = 26
|
||||||
fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), block_count)
|
fout.add_uint32(add_key_str(KEY_BLOCK_COUNT, VISION), block_count)
|
||||||
|
|
||||||
if processor is not None:
|
if processor is not None:
|
||||||
image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean
|
image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean
|
||||||
|
|
@ -1237,6 +1237,7 @@ if has_minicpmv_projector:
|
||||||
for nk, nv in kvs.items():
|
for nk, nv in kvs.items():
|
||||||
new_state_dict[nk] = nv
|
new_state_dict[nk] = nv
|
||||||
projector = new_state_dict
|
projector = new_state_dict
|
||||||
|
ftype_cur = 0
|
||||||
for name, data in projector.items():
|
for name, data in projector.items():
|
||||||
name = get_tensor_name(name)
|
name = get_tensor_name(name)
|
||||||
data = data.squeeze().numpy()
|
data = data.squeeze().numpy()
|
||||||
|
|
|
||||||
|
|
@ -1,5 +1,4 @@
|
||||||
import argparse
|
import argparse
|
||||||
import glob
|
|
||||||
import os
|
import os
|
||||||
import torch
|
import torch
|
||||||
from transformers import AutoModel, AutoTokenizer
|
from transformers import AutoModel, AutoTokenizer
|
||||||
|
|
|
||||||
|
|
@ -1,47 +0,0 @@
|
||||||
import argparse
|
|
||||||
import os
|
|
||||||
import torch
|
|
||||||
from transformers import AutoModel, AutoTokenizer
|
|
||||||
|
|
||||||
ap = argparse.ArgumentParser()
|
|
||||||
ap.add_argument("-m", "--model", help="Path to MiniCPM-V-2.5 model")
|
|
||||||
args = ap.parse_args()
|
|
||||||
|
|
||||||
# find the model part that includes the the multimodal projector weights
|
|
||||||
model = AutoModel.from_pretrained(args.model, trust_remote_code=True, local_files_only=True)
|
|
||||||
checkpoint = model.state_dict()
|
|
||||||
|
|
||||||
# get a list of mm tensor names
|
|
||||||
mm_tensors = [k for k, v in checkpoint.items() if k.startswith("resampler")]
|
|
||||||
|
|
||||||
# store these tensors in a new dictionary and torch.save them
|
|
||||||
projector = {name: checkpoint[name].float() for name in mm_tensors}
|
|
||||||
torch.save(projector, f"{args.model}/minicpmv.projector")
|
|
||||||
|
|
||||||
clip_tensors = [k for k, v in checkpoint.items() if k.startswith("vpm")]
|
|
||||||
if len(clip_tensors) > 0:
|
|
||||||
clip = {name.replace("vpm.", ""): checkpoint[name].float() for name in clip_tensors}
|
|
||||||
torch.save(clip, f"{args.model}/minicpmv.clip")
|
|
||||||
|
|
||||||
# added tokens should be removed to be able to convert Mistral models
|
|
||||||
if os.path.exists(f"{args.model}/added_tokens.json"):
|
|
||||||
with open(f"{args.model}/added_tokens.json", "w") as f:
|
|
||||||
f.write("{}\n")
|
|
||||||
|
|
||||||
config = model.llm.config
|
|
||||||
config._name_or_path = "openbmb/MiniCPM-Llama3-V-2.5"
|
|
||||||
config.auto_map = {
|
|
||||||
"AutoConfig": "configuration_minicpm.MiniCPMConfig",
|
|
||||||
"AutoModel": "modeling_minicpm.MiniCPMModel",
|
|
||||||
"AutoModelForCausalLM": "modeling_minicpm.MiniCPMForCausalLM",
|
|
||||||
"AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPMForCausalLM",
|
|
||||||
"AutoModelForSequenceClassification": "modeling_minicpm.MiniCPMForSequenceClassification"
|
|
||||||
}
|
|
||||||
model.llm.save_pretrained(f"{args.model}/model")
|
|
||||||
tok = AutoTokenizer.from_pretrained(args.model, trust_remote_code=True)
|
|
||||||
tok.save_pretrained(f"{args.model}/model")
|
|
||||||
# os.system(f"cp {args.model}/modeling_minicpm.py {args.model}/MiniCPM_l3/modeling_minicpm.py")
|
|
||||||
|
|
||||||
print("Done!")
|
|
||||||
print(f"Now you can convert {args.model} to a regular LLaMA GGUF file.")
|
|
||||||
print(f"Also, use {args.model}/minicpmv.projector to prepare a minicpmv-encoder.gguf file.")
|
|
||||||
Loading…
Add table
Add a link
Reference in a new issue