| import sys, json, os |
| sys.path.insert(0, os.environ.get("INDIGO_DIR", r"D:\Documents\GitHub\Indigo")) |
| sys.stdout.reconfigure(encoding="utf-8", errors="replace") |
| import torch |
| from safetensors.torch import load_file |
| from indigo.common import build_tokenizer |
| from indigo.model import GPT, GPTConfig |
|
|
| ckpt = os.environ.get("CKPT", r"D:\Documents\GitHub\Indigo\runs\indo\indigo.safetensors") |
| meta_path = ckpt.replace(".safetensors", "_meta.json") |
| with open(meta_path, encoding="utf-8") as f: |
| meta = json.load(f) |
| state = load_file(ckpt) |
| model = GPT(GPTConfig.from_dict(meta["config"])) |
| model.load_state_dict(state, strict=False) |
| model.eval() |
| tokenizer = build_tokenizer(meta.get("tokenizer") or {"type": "char"}, meta.get("vocab")) |
| block_size = meta["config"]["block_size"] |
|
|
| history_ids = json.loads(os.environ.get("HISTORY", "[]")) |
| prompt = os.environ.get("PROMPT", "Apa itu Indigo?") |
|
|
| temperature = float(os.environ.get("TEMPERATURE", "0.8")) |
| top_k = int(os.environ.get("TOP_K", "40")) |
| top_p = float(os.environ.get("TOP_P", "0.95")) |
| max_new = int(os.environ.get("MAX_NEW", "200")) |
|
|
| piece = tokenizer.encode("\nAnda: " + prompt + "\nIndigo:") |
| ctx = (history_ids + piece)[-block_size:] |
| x = torch.tensor([ctx], dtype=torch.long) |
| with torch.no_grad(): |
| out = model.generate(x, max_new, temperature=temperature, top_k=top_k, top_p=top_p, repetition_penalty=1.15) |
| full = out[0].tolist() |
| new_ids = full[len(ctx):] |
| text = tokenizer.decode(new_ids).strip() |
| print(json.dumps({"text": text, "ids": full[-block_size:]})) |
|
|
