Initial commit: E4B-MarkBase model integration with passing tests
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- E4B-MarkBase model (42 layers, 4.4GB) loaded successfully - All Phase 1-6 tests passed (model loading, forward pass, vision/audio towers, token generation, performance) - All stress tests passed (5/5 in 127.6s) - Concurrent inference - Memory stress (67.5 tok/s, 0 NaN) - Continuous generation - Batch processing - Long-running stability - Swift Metal inference engine with multimodal support
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MarkBase Admin
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#!/usr/bin/env python3
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"""
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Convert MLX Gemma-4 26B to MarkBase-12B compatible format
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Usage:
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python convert_mlx_26b.py --input ~/.cache/huggingface/hub/models--mlx-community--gemma-4-26b-a4b-mxfp4 --output ~/models/gemma-4-26b-standard
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"""
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import argparse
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import json
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import os
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import shutil
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from pathlib import Path
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try:
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from safetensors.torch import load_file, save_file
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import torch
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except ImportError:
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print("需要安装依赖:")
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print(" pip install safetensors torch")
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exit(1)
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def convert_mlx_to_standard(input_dir: str, output_dir: str):
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"""转换 MLX 26B 为标准格式"""
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print("=== MLX 26B → 标准 4-bit 转换 ===")
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input_path = Path(input_dir)
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output_path = Path(output_dir)
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# 创建输出目录
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output_path.mkdir(parents=True, exist_ok=True)
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print(f"\n步骤 1: 加载 MLX 权重")
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# 加载所有 shards
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weights = {}
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shard_files = [
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"model-00001-of-00003.safetensors",
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"model-00002-of-00003.safetensors",
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"model-00003-of-00003.safetensors"
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]
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for shard in shard_files:
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shard_path = input_path / shard
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if shard_path.exists():
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print(f" 加载 {shard}...")
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shard_weights = load_file(str(shard_path))
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weights.update(shard_weights)
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print(f" ✓ 总权重数: {len(weights)}")
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print(f"\n步骤 2: 重命名权重")
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# 重命名: language_model.model.layers.X → layers.X
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renamed_weights = {}
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for key, tensor in weights.items():
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# 移除 language_model.model 前缀
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new_key = key
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if key.startswith("language_model.model."):
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new_key = key.replace("language_model.model.", "")
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# 处理 embed_tokens (特殊情况)
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if new_key.startswith("embed_tokens"):
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# 保留原名
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pass
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# 处理 vision tower
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if new_key.startswith("embed_vision"):
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# 重命名为 vision_tower
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new_key = new_key.replace("embed_vision", "vision_tower")
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renamed_weights[new_key] = tensor
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if len(renamed_weights) % 100 == 0:
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print(f" 已处理 {len(renamed_weights)}/{len(weights)} 权重")
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print(f" ✓ 重命名完成")
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print(f"\n步骤 3: 转换 scales 格式")
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# uint8 scales → BF16
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converted_weights = {}
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for key, tensor in renamed_weights.items():
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if ".scales" in key and tensor.dtype == torch.uint8:
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# uint8 → float32 → bfloat16
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converted = tensor.float().bfloat16()
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converted_weights[key] = converted
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print(f" 转换 {key}: uint8 → BF16")
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else:
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converted_weights[key] = tensor
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print(f" ✓ scales 转换完成")
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print(f"\n步骤 4: 保存为单个 safetensors")
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# 合并为单个文件
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output_weights_file = output_path / "model.safetensors"
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save_file(converted_weights, str(output_weights_file))
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print(f" ✓ 保存到: {output_weights_file}")
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print(f"\n步骤 5: 创建 config.json")
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# 加载原始 config
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config_path = input_path / "config.json"
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if config_path.exists():
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with open(config_path) as f:
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mlx_config = json.load(f)
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# 获取 text_config
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text_config = mlx_config.get("text_config", {})
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# 创建标准 config
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standard_config = {
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"model_type": "gemma4",
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"architectures": ["Gemma4ForConditionalGeneration"],
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"hidden_size": text_config.get("hidden_size", 2816),
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"num_hidden_layers": 42, # 需要从权重推断
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"vocab_size": 262144,
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"intermediate_size": text_config.get("intermediate_size", 2112),
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"head_dim": text_config.get("head_dim", 256),
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"num_attention_heads": 8,
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"num_key_value_heads": 2,
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"max_position_embeddings": 131072,
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"quantization_config": {
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"bits": 4,
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"group_size": 64,
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"quant_method": "custom"
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}
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}
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# 添加 vision config (如果有)
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vision_config = mlx_config.get("vision_config", {})
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if vision_config:
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standard_config["vision_config"] = {
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"hidden_size": vision_config.get("hidden_size", 768),
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"num_hidden_layers": 16,
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"patch_size": 16,
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"image_size": 224
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}
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# 写入 config
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with open(output_path / "config.json", "w") as f:
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json.dump(standard_config, f, indent=2)
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print(f" ✓ config.json 创建完成")
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print(f"\n步骤 6: 复制 tokenizer 文件")
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# 复制 tokenizer 相关文件
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tokenizer_files = [
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"tokenizer.json",
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"tokenizer_config.json",
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"generation_config.json",
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"chat_template.jinja"
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]
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for file in tokenizer_files:
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src = input_path / file
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if src.exists():
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dst = output_path / file
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shutil.copy2(src, dst)
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print(f" ✓ 复制 {file}")
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print(f"\n=== 转换完成 ===")
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print(f"输出目录: {output_path}")
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print(f"总权重: {len(converted_weights)}")
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# 显示文件列表
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print(f"\n输出文件:")
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for file in sorted(output_path.iterdir()):
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if file.is_file():
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size = file.stat().st_size / 1024 / 1024
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print(f" {file.name}: {size:.1f} MB")
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print(f"\n下一步:")
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print(f" swift run G12BServer {output_dir} 8080 gemma-26b")
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print(f" 或测试:")
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print(f" swift test --filter test26BModelLoading")
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def main():
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parser = argparse.ArgumentParser(description="转换 MLX 26B 为标准格式")
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parser.add_argument("--input", required=True, help="MLX 模型目录")
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parser.add_argument("--output", required=True, help="输出目录")
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args = parser.parse_args()
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convert_mlx_to_standard(args.input, args.output)
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if __name__ == "__main__":
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main()
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