Qrzysztof/functiongemma-prepaid-cards-tool-calling-v2

FunctionGemma Prepaid-Cards Tool-Calling Dataset

Synthetic training data for fine-tuning google/functiongemma-270m-it so it can recognize chat intents for buying prepaid cards, checking balances, and viewing transaction history, and emit the correct tool call.

Tools

Tool	Purpose
purchase_card(amount, card_type, email?, currency?)	Buy a Digital Prepaid Visa or Virtual Prepaid Mastercard
get_card_balance(card_number)	Check the balance of a card
get_transaction_history(card_number, limit?)	List a card's transactions

card_type values: digital_prepaid_visa | virtual_prepaid_mastercard.

Format

Each row follows the exact structure TRL's SFTTrainer expects for FunctionGemma:

• messages — conversation in the OpenAI-style format (developer / user / assistant with tool_calls / tool responses)
• tools — JSON schemas generated with transformers.utils.get_json_schema
• lang — ISO 639-1 code of the user messages
• kind — sample category (see below)
• template_id — deterministic id used for the train/test split
• split — train or test (deterministic)

Sample kinds

• purchase — single-turn card purchase requests
• balance — single-turn balance requests (full, masked ****1234, 1234...9876, ending in 1234 card numbers)
• tx — single-turn transaction-history requests
• chain_split — multi-turn: card number given in message 1, request in message 2
• chain_clarify — multi-turn: request without card number → assistant asks → user provides → tool call
• chain_purchase_clarify — multi-turn purchase: amount/type collected across turns
• chain_retry — tool called with missing arg → error response → clarification → retry
• chain_full / chain_full_balance / chain_full_tx — full function-calling loop: call → response → assistant text
• chain_two / chain_purchase_balance — long multi-tool conversations
• greet / negative — small talk that must not trigger a tool call

Languages

User messages are hand-translated in 84 languages (list in the language tag above). English has the largest template set (60+ purchase phrasings, 45 balance, 45 transaction).

Train/test split

• 5 languages are fully held out of training: ja, ko, ar, sw, ur
• ~12% of English templates are held out
• Split is deterministic per template_id

Build

python3 build_dataset.py --push

Training

See the companion training script in this repository (train.py) which follows the official FunctionGemma fine-tuning guide.

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v2 dataset (...-tool-calling-v2)

Same tools and format as v1, plus:

• 22 new languages (106 total): Odia, Assamese, Sindhi, Kirundi, Luganda, Chichewa, Shona, Sesotho, Tswana, Tsonga, Malagasy, Fijian, Tongan, Hawaiian, Turkmen, Tatar, Bashkir, Chechen, Ossetian, Kurmanji Kurdish, Guarani, Quechua
• Realistic user noise — like real chat users type:
  • typos (letter swaps, dropped/duplicated letters)
  • lowercase/capslock, dropped punctuation, ??/!!!/...
  • text-speak & misspellings (en: plz, wanna, thx; es: xfa, xq; vi: diacritic-free typing; th: ได้มั้ย; ja/ko/zh casual forms; …)
  • dropped articles, dropped short words, repeated words
  • scrambled word order (hand-written "broken" templates for en, es, fr, de, pt, ru, zh, ja, ar, id, tr)
  • noise is digit-safe: card numbers and amounts are never modified
• New noise column: clean / light / medium / heavy per sample (distribution ≈ 42/30/19/9%)

Fine-tuning tutorial

A complete, minimal fine-tune of a FunctionGemma-class model on this data (follows the official FunctionGemma fine-tuning guide).

1. Setup

pip install torch transformers trl datasets accelerate
huggingface-cli login   # accept the gemma license for google/functiongemma-270m-it

2. Load the dataset and normalize messages

The Hub dataset stores messages/tools as JSON strings (Arrow cannot infer the nested schema), and TRL's SFTTrainer needs a uniform struct schema, so normalize first:

import json
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer

def normalize_messages(msgs):
    out = []
    for m in msgs:
        n = {"role": m["role"], "content": m.get("content") or "", "name": None,
             "tool_call_id": m.get("tool_call_id"), "tool_calls": None}
        if m["role"] == "tool":
            n["name"] = m["content"]["name"]
            n["content"] = json.dumps(m["content"]["response"], ensure_ascii=False)
        if m.get("tool_calls"):
            n["tool_calls"] = [{"id": tc.get("id"), "type": tc.get("type", "function"),
                                "function": {"name": tc["function"]["name"],
                                             "arguments": json.dumps(tc["function"]["arguments"], ensure_ascii=False)}}
                               for tc in m["tool_calls"]]
        out.append(n)
    return out

def rows_to_dataset(rows):
    from datasets import Dataset
    return Dataset.from_list([{
        "messages": normalize_messages(r["messages"]),
        "tools": json.dumps(r["tools"], ensure_ascii=False),
    } for r in rows])

ds = load_dataset("Qrzysztof/ecommerce-chat-tool-calling", token=HF_TOKEN)["train"]
train_rows = [{"messages": json.loads(r["messages_json"]), "tools": json.loads(r["tools_json"])}
              for r in ds if r["split"] == "train"]
train_ds = rows_to_dataset(train_rows)

3. Train

import torch
from transformers import AutoModelForCausalLM
from trl import SFTConfig, SFTTrainer

model = AutoModelForCausalLM.from_pretrained("google/functiongemma-270m-it",
                                             dtype=torch.bfloat16, attn_implementation="eager")
tokenizer = AutoTokenizer.from_pretrained("google/functiongemma-270m-it")

trainer = SFTTrainer(
    model=model,
    args=SFTConfig(
        output_dir="functiongemma-ecommerce",
        max_length=1024,          # covers the longest sample + margin
        packing=False,            # keep tool calls intact (no cross-sample packing)
        num_train_epochs=3,
        per_device_train_batch_size=8,
        learning_rate=5e-5,
        lr_scheduler_type="constant",
        warmup_steps=50,
        bf16=True,                # or fp16 on non-Ampere GPUs
        eval_strategy="epoch",
        report_to="none",
    ),
    train_dataset=train_ds,
    processing_class=tokenizer,
)
trainer.train()

TRL applies the FunctionGemma chat template with the per-sample tools column; assistant_only_loss=True (default) masks everything but the model's own turns, so it learns to emit tool calls — not to copy the schema.

4. Evaluate (greedy success rate)

ok = 0
for item in test_rows:
    inputs = tokenizer.apply_chat_template(item["messages"][:-1], tools=item["tools"],
                                           add_generation_prompt=True, return_tensors="pt")
    out = model.generate(**inputs, max_new_tokens=256)
    output = tokenizer.decode(out[0][len(inputs["input_ids"][0]):], skip_special_tokens=False)
    expected = <expected tool name / args from expected_json>
    ok += expected-tool-in-output and no-other-tool-in-output

5. Push

trainer.push_to_hub("YOUR_USER/functiongemma-ecommerce")

Best practices

Data

• Keep noise digit-safe: never corrupt the values the model must extract (prices, ids). The noise.py engine skips any token containing digits.
• Use deterministic train/test splits (by template_id) and hold out whole languages + (for the e-commerce set) whole schemas — that is the only honest way to measure generalization.
• Balance the training subset per (language, intent) — cap the big buckets instead of letting English dominate.

Training

• packing=False for tool-calling data; packed sequences splice mid-call.
• max_length ≥ longest sample + a margin; ~1024 covers these datasets.
• Constant LR + short warmup (the official guide's defaults) work well.
• Upload a checkpoint to the Hub after every epoch — Colab VMs die mid-run, and the last good epoch is always recoverable.

Evaluation

• Always evaluate with greedy decoding for comparability across formats and runs.
• Score two things separately: tool-name selection and argument fidelity (query + every filter key:value pair).
• Compare every exported format (SafeTensors / GGUF / MLX / ONNX) on the same prompts — quantization changes results.

Deployment

• Validate tool arguments server-side before executing anything (a small model can garble a card number under heavy noise).
• In a live agent, follow the FunctionGemma full loop: model call → backend executes → tool response → model continues; never let the model see or emit secrets.
• For browser deployment use the fp16 ONNX file; for low-end hardware the Q8_0 GGUF or MLX 8-bit; for exact reference behavior the SafeTensors model.