初始化项目，由ModelHub XC社区提供模型

Model: TobiasLogic/TextModel-v1
Source: Original Platform

build_png_release.py

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+import json
+import math
+import os
+import shutil
+
+import numpy as np
+import torch
+from PIL import Image
+from safetensors.torch import load_file
+from transformers import LlamaConfig, LlamaForCausalLM
+from huggingface_hub import hf_hub_download, HfApi
+
+def build_model(c):
+    return LlamaForCausalLM(LlamaConfig(**{k: c[k] for k in [
+        "vocab_size", "hidden_size", "intermediate_size", "num_hidden_layers",
+        "num_attention_heads", "num_key_value_heads", "max_position_embeddings",
+        "rms_norm_eps", "tie_word_embeddings",
+    ]}))
+
+def encode(weights, png_path, cfg_path, config="config.json"):
+    c = json.load(open(config))
+    model = build_model(c)
+    model.load_state_dict(load_file(weights), strict=False)
+    parts, manifest = [], []
+    for name, p in model.named_parameters():
+        a = p.detach().to(torch.float16).contiguous().view(-1).numpy()
+        parts.append(a)
+        manifest.append({"name": name, "shape": list(p.shape), "numel": int(a.size)})
+    flat = np.concatenate(parts)
+    N = flat.size
+    side = math.ceil(math.sqrt(N))
+    u16 = flat.view(np.uint16)
+    img = np.zeros((side * side, 3), dtype=np.uint8)
+    img[:N, 0] = (u16 >> 8).astype(np.uint8)
+    img[:N, 1] = (u16 & 0xFF).astype(np.uint8)
+    Image.fromarray(img.reshape(side, side, 3), "RGB").save(png_path)
+    total = sum(m["numel"] for m in manifest)
+    json.dump({"cfg": c, "params": manifest, "total_parameters": total,
+               "side": side, "dtype": "float16", "channels": "R=hi,G=lo,B=unused"},
+              open(cfg_path, "w"))
+    mb = os.path.getsize(png_path) / 1e6
+    print(f"[png] encoded {total:,} params -> {side}x{side} PNG ({mb:.1f} MB)")
+    return total, side
+
+def load_model_png(png_path, cfg_path, device="cpu"):
+    meta = json.load(open(cfg_path))
+    model = build_model(meta["cfg"])
+    arr = np.asarray(Image.open(png_path).convert("RGB")).reshape(-1, 3)
+    total = meta["total_parameters"]
+    hi = arr[:total, 0].astype(np.uint16)
+    lo = arr[:total, 1].astype(np.uint16)
+    flat = ((hi << 8) | lo).astype(np.uint16).view(np.float16)
+    sd = dict(model.named_parameters())
+    off = 0
+    with torch.no_grad():
+        for m in meta["params"]:
+            n = m["numel"]
+            chunk = flat[off:off + n].astype(np.float16)
+            sd[m["name"]].copy_(torch.from_numpy(chunk.copy()).view(*m["shape"]).to(torch.float32))
+            off += n
+    return model.to(device).eval()
+
+def verify(weights, png_path, cfg_path, config="config.json"):
+    model = load_model_png(png_path, cfg_path)
+    ref = load_file(weights)
+    worst, name = 0.0, ""
+    for k, p in model.named_parameters():
+        d = (p.detach() - ref[k].float()).abs().max().item()
+        if d > worst:
+            worst, name = d, k
+    rel = worst / max(1e-12, ref[name].float().abs().max().item())
+    print(f"[png] round-trip max abs err {worst:.3e} on {name} (relative {rel:.2e})")
+    return worst
+
+repo = "TobiasLogic/textmodel-gci-scratch-ckpt"
+rev = "step50000"
+cfg_path = hf_hub_download(repo_id=repo, revision=rev, filename="config.json")
+w_path = hf_hub_download(repo_id=repo, revision=rev, filename="model.safetensors")
+shutil.copy(cfg_path, "config.json")
+shutil.copy(w_path, "model.safetensors")
+
+total, side = encode("model.safetensors", "model.png", "model_png.json", "config.json")
+worst = verify("model.safetensors", "model.png", "model_png.json", "config.json")
+print("total params", total, "side", side, "worst abs err", worst)
+
+api = HfApi()
+api.upload_file(path_or_fileobj="model.png", path_in_repo="model.png", repo_id="TobiasLogic/TextModel-v1")
+api.upload_file(path_or_fileobj="model_png.json", path_in_repo="model_png.json", repo_id="TobiasLogic/TextModel-v1")
+print("uploaded model.png + model_png.json")