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fix output

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Ray 2025-08-26 19:10:07 +08:00
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cookbook/pageindex_RAG_simple.ipynb
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@ -211,7 +211,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@ -257,7 +257,7 @@
},
{
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"execution_count": 41,
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@ -369,7 +369,7 @@
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 21,
"metadata": {
"id": "LLHNJAtTcG1O"
},
@ -409,7 +409,7 @@
},
{
"cell_type": "code",
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"execution_count": 57,
"metadata": {
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"base_uri": "https://localhost:8080/",
@ -424,12 +424,13 @@
"output_type": "stream",
"text": [
"Reasoning Process:\n",
"The question asks for the conclusions in the document. The most direct and relevant node is '5.\n",
"Conclusion, Limitations, and Future Work' (node_id: 0019), as it is specifically dedicated to the\n",
"conclusion and related topics. Other nodes, such as the Abstract (0001), Introduction (0003), and\n",
"Discussion (0018), may contain summary statements or high-level findings, but the explicit\n",
"conclusions are most likely found in node 0019. Therefore, node 0019 is the primary node likely to\n",
"contain the answer.\n",
"The question asks for the conclusions in the document. Typically, conclusions are found in sections\n",
"explicitly titled 'Conclusion' or in sections summarizing the findings and implications of the work.\n",
"In this document tree, node 0019 ('5. Conclusion, Limitations, and Future Work') is the most\n",
"directly relevant, as it is dedicated to the conclusion and related topics. Additionally, the\n",
"'Abstract' (node 0001) may contain a high-level summary that sometimes includes concluding remarks,\n",
"but it is less likely to contain the full conclusions. Other sections like 'Discussion' (node 0018)\n",
"may discuss implications but are not explicitly conclusions. Therefore, the primary node is 0019.\n",
"\n",
"Retrieved Nodes:\n",
"Node ID: 0019\t Page: 16\t Title: 5. Conclusion, Limitations, and Future Work\n"
@ -467,7 +468,7 @@
},
{
"cell_type": "code",
"execution_count": 27,
"execution_count": 58,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@ -519,7 +520,7 @@
},
{
"cell_type": "code",
"execution_count": 28,
"execution_count": 59,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@ -535,18 +536,18 @@
"text": [
"Generated Answer:\n",
"\n",
"**Conclusions in this document:**\n",
"The conclusions in this document are:\n",
"\n",
"- DeepSeek-R1-Zero, a pure reinforcement learning (RL) model without cold-start data, achieves\n",
"- DeepSeek-R1-Zero, a pure reinforcement learning (RL) approach without cold-start data, achieves\n",
"strong performance across various tasks.\n",
"- DeepSeek-R1, which combines cold-start data with iterative RL fine-tuning, is even more powerful\n",
"and achieves performance comparable to OpenAI-o1-1217 on a range of tasks.\n",
"- The reasoning capabilities of DeepSeek-R1 can be successfully distilled into smaller dense models,\n",
"with DeepSeek-R1-Distill-Qwen-1.5B outperforming GPT-4o and Claude-3.5-Sonnet on math benchmarks.\n",
"- Other small dense models fine-tuned with DeepSeek-R1 data also significantly outperform other\n",
"instruction-tuned models based on the same checkpoints.\n",
"- Overall, the approaches described demonstrate promising results in enhancing model reasoning\n",
"abilities through RL and distillation.\n"
"- DeepSeek-R1, which combines cold-start data with iterative RL fine-tuning, is more powerful and\n",
"achieves performance comparable to OpenAI-o1-1217 on a range of tasks.\n",
"- Distilling DeepSeek-R1’s reasoning capabilities into smaller dense models is promising; for\n",
"example, DeepSeek-R1-Distill-Qwen-1.5B outperforms GPT-4o and Claude-3.5-Sonnet on math benchmarks,\n",
"and other dense models also show significant improvements over similar instruction-tuned models.\n",
"\n",
"These results demonstrate the effectiveness of the RL-based approach and the potential for\n",
"distilling reasoning abilities into smaller models.\n"
]
}
],