初始化项目，由ModelHub XC社区提供模型

Model: divakar-yadav/transformer-1b-chat
Source: Original Platform

training_code/inference.py

@@ -0,0 +1,134 @@
+"""
+Inference script for the 1B Transformer — Single GPU.
+
+Usage:
+  python inference.py                          # auto-finds latest checkpoint
+  python inference.py /path/to/checkpoint.pt   # specific checkpoint
+"""
+
+import sys
+import os
+import glob
+import time
+import torch
+import torch.nn.functional as F
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+from model.config import ModelConfig
+from model.transformer import Transformer
+from model.data import get_tokenizer
+
+
+def find_latest_checkpoint(checkpoint_dir="/jfs/deepak-kumar/checkpoints"):
+    files = glob.glob(os.path.join(checkpoint_dir, "step_*.pt"))
+    if not files:
+        final = os.path.join(checkpoint_dir, "final.pt")
+        return final if os.path.exists(final) else None
+    return max(files, key=lambda f: int(os.path.basename(f).split("_")[1].split(".")[0]))
+
+
+def load_model(checkpoint_path, device="cuda:0"):
+    config = ModelConfig()
+    model = Transformer(config)
+
+    print(f"Loading checkpoint: {checkpoint_path}")
+    ckpt = torch.load(checkpoint_path, map_location="cpu", weights_only=False)
+    model.load_state_dict(ckpt["model"])
+    model = model.to(device).bfloat16().eval()
+
+    step = ckpt.get("step", "?")
+    loss = ckpt.get("loss", "?")
+    print(f"  Step: {step} | Loss: {loss}")
+    print(f"  Params: {sum(p.numel() for p in model.parameters()):,}")
+    print(f"  Device: {device}")
+    del ckpt
+    torch.cuda.empty_cache()
+    return model, config
+
+
+@torch.no_grad()
+def generate(model, tokenizer, prompt, max_new_tokens=200,
+             temperature=0.8, top_k=50, top_p=0.9, device="cuda:0"):
+    input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
+    t0 = time.time()
+
+    for i in range(max_new_tokens):
+        if input_ids.shape[1] >= model.config.max_seq_len:
+            break
+
+        with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+            logits, _ = model(input_ids)
+
+        logits = logits[:, -1, :] / temperature
+
+        if top_k > 0:
+            topk_vals, _ = torch.topk(logits, top_k)
+            logits[logits < topk_vals[:, -1:]] = float("-inf")
+
+        if top_p < 1.0:
+            sorted_logits, sorted_idx = torch.sort(logits, descending=True)
+            cum_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+            mask = cum_probs - F.softmax(sorted_logits, dim=-1) >= top_p
+            sorted_logits[mask] = float("-inf")
+            logits = sorted_logits.scatter(1, sorted_idx, sorted_logits)
+
+        probs = F.softmax(logits, dim=-1)
+        next_token = torch.multinomial(probs, num_samples=1)
+
+        if next_token.item() == tokenizer.eos_token_id:
+            break
+
+        input_ids = torch.cat([input_ids, next_token], dim=1)
+
+    elapsed = time.time() - t0
+    gen_tokens = input_ids.shape[1] - len(tokenizer.encode(prompt))
+    tok_per_sec = gen_tokens / max(elapsed, 1e-9)
+
+    text = tokenizer.decode(input_ids[0], skip_special_tokens=True)
+    return text, gen_tokens, tok_per_sec
+
+
+def main():
+    device = "cuda:0"
+    if len(sys.argv) > 1:
+        checkpoint = sys.argv[1]
+    else:
+        checkpoint = find_latest_checkpoint()
+        if checkpoint is None:
+            print("No checkpoint found!")
+            sys.exit(1)
+
+    model, config = load_model(checkpoint, device)
+    tokenizer = get_tokenizer()
+
+    prompts = [
+        "The meaning of life is",
+        "In machine learning, a neural network",
+        "The capital of France is",
+        "Once upon a time, there was a",
+        "To solve a quadratic equation, you need to",
+        "The theory of relativity explains that",
+        "Python is a programming language that",
+        "The sun rises in the east and",
+    ]
+
+    print("\n" + "=" * 70)
+    print("  INFERENCE — 1B Transformer (Single GPU)")
+    print("=" * 70)
+
+    for prompt in prompts:
+        print(f"\n{'─' * 60}")
+        print(f"PROMPT: {prompt}")
+        print(f"{'─' * 60}")
+        text, n_tok, tps = generate(model, tokenizer, prompt,
+                                     max_new_tokens=150, temperature=0.8,
+                                     top_k=50, device=device)
+        generated = text[len(prompt):]
+        print(f"OUTPUT:{generated}")
+        print(f"  [{n_tok} tokens, {tps:.1f} tok/s]")
+
+    print("\n" + "=" * 70)
+
+
+if __name__ == "__main__":
+    main()