AmanPriyanshu/tool-reasoning-sft-CODING-nvidia-Nemotron-Agentic-v1

Nemotron-Agentic-v1 — Cleaned & Rectified

335k multi-turn agentic tool-use trajectories from NVIDIA's Nemotron-Agentic-v1, converted into a strict reasoning + tool-call format with validated FSM transitions.

Origin

Derived from nvidia/Nemotron-Agentic-v1.

Nemotron-Agentic-v1 is a synthetic dataset of multi-turn conversations where language models decompose user goals, decide when to call tools, and reason over tool outputs. Trajectories are generated by simulating user, agent, and tool-execution roles using Qwen3-235B-A22B-Thinking, Qwen3-32B, GPT-OSS-120B, and Qwen3-235B-A22B-Instruct, with turn-level quality judgments to filter inconsistent or incorrect tool use.

Format

Each row contains a structured multi-turn conversation with explicit reasoning traces and validated tool calls.

Message Roles

Role	Content
system	Tool-use protocol + cleaned JSON tool schemas + domain instructions
user	User request or follow-up in multi-turn dialogue
reasoning	<think>…</think> — model's step-by-step reasoning
tool_call	<tool_call>{"name": "...", "arguments": {...}}</tool_call> — function invocation
tool_output	<tool_response>…</tool_response> — tool execution result
answer	<answer>…</answer> — agent's response to the user

Trajectory Structure

system → user → reasoning → [tool_call → tool_output → reasoning →]* answer → [user → reasoning → ...]

Conversations range from 4 to 166 turns (avg 12.6), with 0–54 tool calls per row (avg 2.1).

Schema

Single Parquet file with zstd compression.

Column	Type	Description
messages	string	Converted conversation (JSON list of {role, content})
uuid	string	Original row UUID from Nemotron-Agentic-v1
split	string	Source subset: interactive_agent or tool_calling

Data Distribution

Split	Source Rows	Converted Rows	Pass Rate
interactive_agent	19,028	19,028	100.00%
tool_calling	316,094	312,166	98.76%
Total	335,122	331,194	98.83%

Failure Reasons (tool_calling split only)

Reason	Count	% of Failures
tool_call→tool_call transition	3,229	82.2%
tool_call→reasoning transition	698	17.8%
reasoning→user transition	1	0.0%

Usage

import json, random
from huggingface_hub import hf_hub_download
import pyarrow.parquet as pq

REPO = "AmanPriyanshu/tool-reasoning-sft-nvidia-Nemotron-Agentic-v1"

print("Downloading data.parquet...")
local = hf_hub_download(REPO, "data.parquet", repo_type="dataset")
t = pq.read_table(local)
print(f"Rows: {t.num_rows:,} | Cols: {t.column_names}\n")

from collections import Counter
splits = [s.as_py() for s in t.column("split")]
print(f"Splits: {dict(Counter(splits).most_common())}\n")

idx = random.randint(0, t.num_rows - 1)
row = {col: t.column(col)[idx].as_py() for col in t.column_names}
msgs = json.loads(row["messages"])
roles = [m["role"] for m in msgs]

print(f"Row {idx} | uuid={row['uuid']} | split={row['split']} | {len(msgs)} turns")
print(f"Roles: {' -> '.join(roles)}\n")

for m in msgs:
    content = m["content"]
    if m["role"] == "system":
        content = content[:200] + "..."
    elif len(content) > 400:
        content = content[:400] + "..."
    print(f"[{m['role']}]")
    print(content)
    print()

Subset Characteristics

interactive_agent (19,028 rows)

• Domain: Customer service (food delivery, event ticketing)
• Reasoning: All synthesized from template pools (source reasoning_content is always empty)
• Tool args: JSON strings parsed to dicts
• Multi-turn: 2–5 user turns per conversation, model learns when NOT to call tools
• Tools: 14 fixed tools per row (domain-specific: authenticate_user, get_order_status, etc.)

tool_calling (312,166 rows)

• Domain: General-purpose API usage (search, finance, utilities, social media, etc.)
• Reasoning: Rich native chain-of-thought (avg 2,562 chars, no <think> tags in source)
• Tool args: Already dicts (passed through directly)
• Tools: 1–60 per row (avg 5.6), diverse tool schemas
• Pre-filtered: reasoning="on" rows only (100% of interactive_agent, 100% of tool_calling)

Conversion Details

• OpenAI-style tool_calls (with function.name + function.arguments) parsed into canonical {"name", "arguments": dict} format
• reasoning_content field on assistant messages → <think>...</think> reasoning turns
• When reasoning_content is empty (interactive_agent), bridge reasoning synthesized from 12-variation template pools
• Tool content handled as string, dict, or list (all serialized to JSON)
• Tool output matching via positional look-ahead from assistant message
• strict field stripped from tool schemas
• Bridge reasoning inserted for all forbidden transitions: tool_output→tool_call, tool_output→answer, user→tool_call, user→answer
• Consecutive reasoning turns merged
• Ending fixes: conversations always terminate with answer role