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- Apache 2.0 License - Zen Research DAO under Zoo Labs Inc (501(c)(3)) - San Francisco-based non-profit for open AI research - Copyright 2025 Zen Research Authors
38 lines
1.1 KiB
Python
38 lines
1.1 KiB
Python
#!/usr/bin/env python3.13
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"""Test base Qwen3 model to verify it works"""
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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# Test base model
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base_path = "/Users/z/work/zen/zen-eco/base-model"
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print("Loading base Qwen3-4B-Instruct model...")
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tokenizer = AutoTokenizer.from_pretrained(base_path, trust_remote_code=True)
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device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
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print(f"Using device: {device}")
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model = AutoModelForCausalLM.from_pretrained(
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base_path,
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torch_dtype=torch.float16,
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trust_remote_code=True
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).to(device)
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print("Testing generation...")
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prompt = "Who are you?"
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inputs = tokenizer(prompt, return_tensors="pt").to(device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=50,
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temperature=0.7,
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do_sample=True,
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pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(f"Prompt: {prompt}")
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print(f"Response: {response}")
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print("\n✅ Base model works!" if len(response) > len(prompt) else "❌ Base model failed") |