sleepyeldrazi/pi-sft-poc

Pi Harness SFT Dataset — PoC (Single-Day Test)

Small, balanced subset for rapid prototyping on a DGX Spark. Designed to validate format adherence, thinking-channel formation, and tool-calling in a single training day.

Design

Follows staged curriculum with two configs:

Config	Rows	Description
no-tools	8,000	Math, coding, reasoning, world-knowledge — reasoning foundation
tools	8,000	6,400 tool-calling trajectories + 1,600 no-tool replay (20%)
default	16,000	Both combined

All rows fit within 16,000 tokens (no truncation).

Usage

from datasets import load_dataset

# Phase A — reasoning foundation
ds_a = load_dataset('sleepyeldrazi/pi-sft-poc', 'no-tools', split='train')

# Phase B — tools with replay
ds_b = load_dataset('sleepyeldrazi/pi-sft-poc', 'tools', split='train')

# Or everything combined
ds = load_dataset('sleepyeldrazi/pi-sft-poc', split='train')

Format

Messages are a list of dicts with a uniform struct: every message carries role, content, reasoning_content, name, tool_call_id (empty strings when not applicable) so the file loads under datasets without schema conflicts.

Tool calls are pre-rendered into the assistant content in the model's own format rather than carried as a separate tool_calls field. An assistant turn that calls a function has the call appended to its content as:

<tool_call>
<function=search>
<parameter=q>query text</parameter>
</function>
</tool_call>

Tool results use role: "tool" (the chat template wraps the content in <tool_response>...</tool_response>). Rendering the dataset through the Qwen3.5 chat template reproduces the canonical <tool_call> / <tool_response> training text directly.

[
  {"role": "system", "content": "<pi harness prompt>", "reasoning_content": "", "name": "", "tool_call_id": ""},
  {"role": "user", "content": "...", "reasoning_content": "", "name": "", "tool_call_id": ""},
  {"role": "assistant", "content": "\n<tool_call>\n<function=search>\n<parameter=q>...</parameter>\n</function>\n</tool_call>", "reasoning_content": "", "name": "", "tool_call_id": ""},
  {"role": "tool", "content": "...", "reasoning_content": "", "name": "search", "tool_call_id": ""}
]

Format history:

• v1: tool_calls stored as a JSON string — crashed chat templates ('str object' has no attribute 'name').
• v2: tool_calls as real lists — broke datasets 4.x schema inference (heterogeneous nested arguments types + date strings auto-typed as timestamps).
• v3 (current): tool calls pre-rendered into assistant content; uniform message struct. Loads cleanly under datasets 4.3.0+ and renders correctly through the Qwen3.5 chat template. Validated: all 15,953 rows render with 0 errors / 0 empty.

Source Breakdown

Source	Rows	Type
user_parquet	5,808	📝 Math, coding, reasoning, world-knowledge
toolmind_open	3,358	🔧 Tool-calling conversations
toolmind_graph_syn	2,649	🔧 Synthetic tool-calling
gemma4_sft	2,567	📝 Pi harness coding traces
user_qwen_distill	1,225	📝 Qwen-distilled reasoning
hermes_kimi	99	🔧 Kimi agent trajectories
openseeker	98	🔧 OpenSeeker tool-reasoning
qwen_trajectory	98	🔧 Qwen agent trajectories
hermes_glm	98	🔧 GLM agent trajectories