初始化项目，由ModelHub XC社区提供模型

Model: OdaxAI/DANTE-Mosaic-3.5B
Source: Original Platform

example_inference.py

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+"""
+Example inference for DANTE-Mosaic-3.5B.
+
+Usage:
+    python example_inference.py
+    python example_inference.py --model YourOrg/DANTE-Mosaic-3.5B
+    python example_inference.py --model ./local_path/
+
+Run on a single A100 / RTX 4090 / H100. ~5.8 GB VRAM in BF16.
+"""
+from __future__ import annotations
+
+import argparse
+import time
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+PROMPTS = [
+    ("MATH", "What is the derivative of f(x) = x^3 + 2x^2 - 5x + 1? Show step by step."),
+    ("CODE", "Write a Python function that checks if a string is a palindrome. Include a docstring and edge cases."),
+    ("LOGIC", "A bat and a ball cost $1.10 in total. The bat costs $1.00 more than the ball. How much does the ball cost? Explain."),
+    ("ITA", "Spiega cos'è il machine learning in termini semplici, adatti a uno studente delle superiori."),
+]
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument("--model", default="./",
+                   help="HF repo id or local path to the model directory")
+    p.add_argument("--max-new-tokens", type=int, default=256)
+    p.add_argument("--temperature", type=float, default=0.7)
+    p.add_argument("--top-p", type=float, default=0.9)
+    args = p.parse_args()
+
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    print(f"Loading {args.model} on {device} ...")
+    t0 = time.time()
+    tok = AutoTokenizer.from_pretrained(args.model, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(
+        args.model,
+        torch_dtype=torch.bfloat16,
+        device_map="auto",
+        trust_remote_code=True,
+    ).eval()
+    print(f"Loaded in {time.time()-t0:.1f}s "
+          f"({sum(p.numel() for p in model.parameters())/1e9:.2f}B params)\n")
+
+    for tag, prompt in PROMPTS:
+        print("─" * 60)
+        print(f"[{tag}] {prompt}\n")
+        inputs = tok(prompt, return_tensors="pt").to(model.device)
+        plen = inputs["input_ids"].shape[-1]
+        t0 = time.time()
+        with torch.no_grad():
+            out = model.generate(
+                **inputs,
+                max_new_tokens=args.max_new_tokens,
+                do_sample=True,
+                temperature=args.temperature,
+                top_p=args.top_p,
+                repetition_penalty=1.1,
+                pad_token_id=tok.eos_token_id,
+            )
+        new_toks = out.shape[-1] - plen
+        elapsed = time.time() - t0
+        text = tok.decode(out[0][plen:], skip_special_tokens=True).strip()
+        print(text)
+        print(f"\n  [{new_toks} tokens in {elapsed:.1f}s — {new_toks/elapsed:.1f} tok/s]\n")
+
+
+if __name__ == "__main__":
+    main()