Cyleux/gemma3n-conversational-reasoning-toolloop
Gemma3N Conversational Reasoning Tool-Loop Gemma3N conversational dataset that preserves tool traces while avoiding training targets on tool responses. Encoding: Assistant emits tool calls: <tool_call ...>...</tool_call> Tool outputs are user-side turns: <tool_response ...>...</tool_response> This works with train_on_responses_only because user-side tool responses are masked from loss. Use: from datasets import load_dataset from unsloth.chat_templates import… See the full description on the dataset page: https://huggingface.co/datasets/Cyleux/gemma3n-conversational-reasoning-toolloop.
Gemma3N Conversational Reasoning Tool-Loop
Gemma3N conversational dataset that preserves tool traces while avoiding training targets on tool responses.
Encoding:
- Assistant emits tool calls:
<tool_call ...>...</tool_call> - Tool outputs are user-side turns:
<tool_response ...>...</tool_response>
This works with train_on_responses_only because user-side tool responses are masked from loss.
Use:
from datasets import load_dataset
from unsloth.chat_templates import standardize_data_formats
dataset = load_dataset("Cyleux/gemma3n-conversational-reasoning-toolloop", split="train[:3000]")
dataset = standardize_data_formats(dataset)Stats:
{
"input": "/Users/oliversilverstein/Documents/boostrapping_intelligence/data/functiongemma_upload/train.jsonl",
"output_train": "/Users/oliversilverstein/Documents/boostrapping_intelligence/data/gemma3n_conversational_reasoning_toolloop/train.jsonl",
"rows": 1214,
"samples_total": 1214,
"rows_written": 1214,
"rows_with_tool_call": 253,
"rows_with_tool_response": 248
}