Update You_can_still_run_it_directly_on_your_own_machine_if_it's_too_small.ipynb
Browse files
You_can_still_run_it_directly_on_your_own_machine_if_it's_too_small.ipynb
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@@ -193,6 +193,49 @@
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"id": "PP6KcuhQXeQ1"
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{
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"cell_type": "code",
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"execution_count": null,
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@@ -229,4 +272,4 @@
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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],
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"id": "PP6KcuhQXeQ1"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "fix1chatFn"
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},
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"outputs": [],
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"source": [
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"# --- FIX: ham chat() bi thieu, day la ham duoc them lai ---\n",
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"# Encode/decode qua tokenizers backend, va tu sinh token cho den khi\n",
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"# gap END_ID (thay vi dung model.generate() goc, vi ham do khong biet\n",
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"# dung lai o END_TOK va se tra ve ca prompt lan phan sinh ra).\n",
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"\n",
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"@torch.no_grad()\n",
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"def chat(user_input, max_new_tokens=200, temperature=0.8, top_k=40):\n",
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" prompt = f\"{USER_TOK}{user_input}{ASSISTANT_TOK}\"\n",
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" prompt_ids = tokenizer_backend.encode(prompt).ids\n",
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" # cat bot neu prompt dai hon block_size\n",
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" prompt_ids = prompt_ids[-model.block_size:]\n",
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" idx = torch.tensor([prompt_ids], dtype=torch.long, device=device)\n",
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"\n",
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" generated_ids = []\n",
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" for _ in range(max_new_tokens):\n",
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" idx_cond = idx[:, -model.block_size:]\n",
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" logits, _ = model(idx_cond)\n",
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" logits = logits[:, -1, :] / temperature\n",
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" if top_k is not None:\n",
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" v, _ = torch.topk(logits, top_k)\n",
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" logits[logits < v[:, [-1]]] = float(\"-inf\")\n",
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" probs = F.softmax(logits, dim=-1)\n",
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" next_id = torch.multinomial(probs, num_samples=1)\n",
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"\n",
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" if next_id.item() == END_ID:\n",
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" break\n",
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"\n",
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" generated_ids.append(next_id.item())\n",
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" idx = torch.cat([idx, next_id], dim=1)\n",
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"\n",
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" reply = tokenizer_backend.decode(generated_ids)\n",
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" return reply.strip()\n"
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],
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"id": "fix1chatFn"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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