agentic-vision
agentic-llm-pretraining-1.7b
Agentic LLM Pretraining Dataset
A pretraining corpus for small language models (1-3B parameters) optimized for agentic tasks. The corpus emphasizes learning to comprehend language, reason, follow instructions, and use tools over memorizing factual knowledge — the assumption is that domain knowledge will be provided at runtime via RAG. The idea is that this could enable much smaller pretraining corpora by omitting the large volumes of text typically needed to memorize facts.… See the full description on the dataset page: https://huggingface.co/datasets/visionscaper/agentic-llm-pretraining-1.7b.agentic-llm-pretraining-1.7b-tokenized-qwen3-4k
Agentic LLM Pretraining Dataset - Tokenized (Qwen3, 4K context)
Pre-tokenized version of visionscaper/agentic-llm-pretraining-1.7b for pre-training small language models for agentic AI use cases.
Overview
Property
Value
Source dataset
visionscaper/agentic-llm-pretraining-1.7b
Tokenizer
Qwen/Qwen3-1.7B
Context length
4,096 tokens
EOD token
<|endoftext|> (ID 151643)
Token dtype
uint32
Total samples
375,384
Total tokens
~1.54 billion
Storage
~5.8 GB… See the full description on the dataset page: https://huggingface.co/datasets/visionscaper/agentic-llm-pretraining-1.7b-tokenized-qwen3-4k.xDAN-Agentic-DeepSearch-example格式：
格式：
{
"data_source": "数据来源标识",
"prompt": [
{
"role": "system",
"content": "系统指令，包含可用工具描述和输出格式要求"
},
{
"role": "user",
"content": "用户查询或请求"
}
],
"ability": "能力标签",
"reward_model": {
"ground_truth": "标准答案，包含<think>、<browse>、<search>、<summarize>、<answer>标签"
"style": "回答风格标签"
},
"extra_info": {
"instruction": "详细指令",
"output": "模型输出",
"split": "train"
}
}
xDAN-Agentic-Data-Instruct-v1-sample