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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
62 lines
1.7 KiB
Python
62 lines
1.7 KiB
Python
#!/usr/bin/env python3.13
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"""Test trained models with simple generation"""
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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import warnings
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warnings.filterwarnings("ignore")
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def test_model(name, path):
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print(f"\n{'='*60}")
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print(f"Testing: {name}")
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try:
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tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
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# Load on CPU first to avoid MPS issues
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model = AutoModelForCausalLM.from_pretrained(
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path,
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torch_dtype=torch.float32,
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device_map="cpu",
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trust_remote_code=True
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)
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# Simple test
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prompt = "Hello"
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inputs = tokenizer(prompt, return_tensors="pt")
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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=10,
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temperature=1.0,
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do_sample=False,
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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"Input: {prompt}")
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print(f"Output: {response}")
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if len(response) > len(prompt):
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print("✅ Model generates text")
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else:
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print("❌ Model doesn't generate")
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except Exception as e:
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print(f"❌ Error: {e}")
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# Test models
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models = [
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("zen-nano", "/Users/z/work/zen/zen-nano/finetuned"),
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("zen-eco-instruct", "/Users/z/work/zen/zen-eco/instruct/finetuned"),
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("zen-eco-thinking", "/Users/z/work/zen/zen-eco/thinking/finetuned"),
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("zen-eco-agent", "/Users/z/work/zen/zen-eco/agent/finetuned"),
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]
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print("TESTING TRAINED MODELS")
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for name, path in models:
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test_model(name, path)
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print("\n" + "="*60)
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print("TEST COMPLETE") |