61 lines
1.8 KiB
Python
61 lines
1.8 KiB
Python
#!/usr/bin/env python3
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"""Inspect the Ornith-1.0-35B model architecture"""
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import torch
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from transformers import AutoModelForCausalLM, AutoConfig
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print("=" * 80)
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print("Inspecting Ornith-1.0-35B model")
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print("=" * 80)
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# Load config
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print("\n1. Loading config...")
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config = AutoConfig.from_pretrained("/data/models/Ornith-1.0-35B", trust_remote_code=True)
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print(f" Config class: {type(config).__name__}")
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print(f" Model type: {config.model_type}")
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# Load model to CPU
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print("\n2. Loading model to CPU...")
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model = AutoModelForCausalLM.from_pretrained(
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"/data/models/Ornith-1.0-35B",
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device_map="cpu",
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torch_dtype=torch.bfloat16,
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trust_remote_code=True,
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low_cpu_mem_usage=True,
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)
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print(f" Model class: {type(model).__name__}")
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# Check if model has quantize_4bit
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print("\n3. Checking for quantization methods...")
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has_quantize = hasattr(model, 'quantize_4bit')
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print(f" Has quantize_4bit(): {has_quantize}")
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# List all model components
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print("\n4. Model components:")
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for name, module in model.named_modules():
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if len(name.split('.')) <= 2: # Top-level and first-level
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print(f" {name}: {type(module).__name__}")
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# Check for BnB quantization support
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print("\n5. Checking BnB support...")
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try:
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from bitsandbytes.nn import Linear4bit, Linear8bitLt
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print(" ✓ BnB 4bit and 8bit modules available")
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except ImportError:
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print(" ✗ BnB not installed")
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# Check if we can use prepare_model_for_kbit_training
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print("\n6. Checking PEFT support...")
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try:
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from peft import prepare_model_for_kbit_training
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print(" ✓ prepare_model_for_kbit_training available")
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except ImportError:
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print(" ✗ PEFT not installed")
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print("\n" + "=" * 80)
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print("Summary:")
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print(f" Model: {type(model).__name__}")
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print(f" Config: {type(config).__name__}")
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print(f" Has quantize_4bit(): {has_quantize}")
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print("=" * 80)
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