43 lines
1.9 KiB
Python
43 lines
1.9 KiB
Python
import os, json, argparse
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HERE = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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os.environ["HF_HOME"] = os.path.join(HERE, "hf_cache")
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--model", default="Qwen/Qwen3-0.6B")
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ap.add_argument("--adapter", default=None)
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ap.add_argument("--eval", default=os.path.join(HERE, "eval", "chat_eval.jsonl"))
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ap.add_argument("--out", default=os.path.join(HERE, "eval", "baseline_chat.json"))
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a = ap.parse_args()
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device = "cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu")
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tok = AutoTokenizer.from_pretrained(a.model)
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model = AutoModelForCausalLM.from_pretrained(a.model, dtype=torch.float32).to(device)
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if a.adapter:
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from peft import PeftModel
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model = PeftModel.from_pretrained(model, a.adapter).to(device)
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model.eval()
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cases = [json.loads(l) for l in open(a.eval) if l.strip()]
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results = []
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for c in cases:
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msgs = [{"role": "user", "content": c["query"]}]
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text = tok.apply_chat_template(msgs, tokenize=False,
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add_generation_prompt=True, enable_thinking=False)
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ids = tok(text, return_tensors="pt").to(device)
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out = model.generate(**ids, max_new_tokens=256, do_sample=False)
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reply = tok.decode(out[0][ids.input_ids.shape[1]:], skip_special_tokens=True)
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results.append({"id": c["id"], "kind": c["kind"],
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"query": c["query"], "reply": reply})
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print(f"[{c['id']}] {c['kind']}: {reply[:80].strip()}...")
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json.dump({"model": a.model, "adapter": a.adapter, "n": len(cases), "results": results},
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open(a.out, "w"), indent=2)
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print(f"\nsaved {len(cases)} chat outputs -> {a.out}")
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if __name__ == "__main__":
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main() |