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Model: ericoh929/qwen3-1.7b-lamini-qlora-instruction-tuned
Source: Original Platform

infer_simple.py

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+# infer_simple.py
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from prompt_format import format_instruction_prompt
+
+MODEL_ID = "ericoh929/qwen3-1.7b-lamini-qlora-instruction-tuned"
+
+def main():
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_ID,
+        trust_remote_code=True,
+        device_map="auto",
+        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    )
+    model.eval()
+
+    instruction = "Answer the question. Be concise."
+    inp = "If Tom has 3 apples and buys 4 more, how many apples does he have?"
+
+    prompt = format_instruction_prompt(instruction, inp)
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+    with torch.inference_mode():
+        out = model.generate(
+            **inputs,
+            max_new_tokens=256,
+            do_sample=False,
+            pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id,
+            eos_token_id=tokenizer.eos_token_id,
+        )
+
+    gen_ids = out[0][inputs["input_ids"].shape[1]:]
+    answer = tokenizer.decode(gen_ids, skip_special_tokens=True).strip()
+
+    print(answer)
+
+if __name__ == "__main__":
+    main()