txn-parser/qwen3-0.6b

license, base_model, tags, language, library_name

license	base_model	tags	language	library_name
apache-2.0	Qwen/Qwen3-0.6B	text-generation lora qlora gguf transaction-parser	en hi	peft

txn-parser / qwen3-0.6b

QLoRA fine-tune of Qwen/Qwen3-0.6B for extracting structured transaction data (amount, currency, item, category, type) from free-form Indian-English / code-switched speech and text.

This model lives in subfolder qwen3-0.6b/ of the kartikey31/txn-parser repo, alongside sibling fine-tunes of other base models trained on the same data.

What's in here

• qwen3-0.6b/adapters/ — PEFT LoRA adapter (rank 32). Load on top of the base model with peft.PeftModel.from_pretrained(base, "kartikey31/txn-parser", subfolder="qwen3-0.6b/adapters").
• qwen3-0.6b/gguf/ — merged GGUF builds at multiple quantization levels (file names follow txn-parser-qwen3-0.6b-<QUANT>.gguf):
  • qwen3-0.6b/gguf/txn-parser-qwen3-0.6b-F16.gguf (1198.2 MB)
  • qwen3-0.6b/gguf/txn-parser-qwen3-0.6b-Q4_K_M.gguf (396.7 MB)
  • qwen3-0.6b/gguf/txn-parser-qwen3-0.6b-Q5_K_M.gguf (444.4 MB)
  • qwen3-0.6b/gguf/txn-parser-qwen3-0.6b-Q6_K.gguf (495.1 MB)
  • qwen3-0.6b/gguf/txn-parser-qwen3-0.6b-Q8_0.gguf (639.4 MB)

Training data

• 93,348 teacher-labeled examples (data/distill/train.jsonl)
• 300 held-out eval examples (data/distill/eval.jsonl)
• Validator-gated: every row's output passes the project's grammar + amount-parser semantic validator.

Training config

Knob	Value
Base model	Qwen/Qwen3-0.6B
Method	QLoRA (4-bit) via Unsloth
LoRA rank	32 (alpha 64, dropout 0.0)
Epochs	2
Batch size (train)	64
Grad accumulation	2
Eval batch size	16
Max seq length	1024
Learning rate	2e-4 (warmup 3%)
Started	2026-05-22T21:54:03.967900+00:00
Finished	2026-05-22T22:42:56.089528+00:00

System prompt (use this EXACTLY)

The model was trained with one specific system prompt and Gemma/Smol/Qwen chat template. If you paraphrase the prompt or skip the chat template, quality degrades quickly. Copy-paste this verbatim into your inference client (no leading/trailing whitespace, no edits):

You convert voice-transcribed transaction descriptions into structured JSON.

Output ONLY a JSON object with this schema, no other text:
{"transactions":[{"amount":<number>,"currency":"INR"|"USD","item":"<lowercase singular noun phrase>","category":"<enum>","type":"expense"|"income"}]}

Categories: Food, Drinks, Groceries, Transport, Shopping, Entertainment, Bills, Health, Education, Personal, Gifts, Income, Other.

Rules:
- Currency defaults to INR. Use USD only when the input explicitly says "dollars" or contains "$".
- Amounts: "k" = ×1000, "hazaar" = ×1000, "sau" = ×100, "lakh" = ×100000. Convert number-words ("five hundred") to digits.
- type is "expense" by default; "income" only for explicit salary, cashback, refund, gift received, payment received.
- For disfluencies and corrections ("500 wait no 600"), output the CORRECTED amount only.
- For ambiguous items ("that thing", "stuff"), use item "unspecified" and category "Other".
- Item field: lowercase singular noun phrase ("uber ride", "beer", "chai" — not "Beers" or "Uber").
- Multi-transaction inputs become multiple array entries in spoken order.
- Category heuristics: uber/ola/auto/petrol/bus/metro → Transport; beer/wine/chai/coffee/juice → Drinks; rent/electricity/wifi/recharge/gas → Bills; movie/netflix/concert → Entertainment; doctor/medicine/hospital → Health.

Source of truth: scripts/_lib.py constant SYSTEM_PROMPT. Don't retype it — pull from _lib.py or this README.

Download a single GGUF

huggingface-cli download kartikey31/txn-parser \
    qwen3-0.6b/gguf/txn-parser-qwen3-0.6b-Q4_K_M.gguf \
    --local-dir .

Inference (Python, llama-cpp-python)

from llama_cpp import Llama

SYSTEM_PROMPT = '''You convert voice-transcribed transaction descriptions into structured JSON.

Output ONLY a JSON object with this schema, no other text:
{"transactions":[{"amount":<number>,"currency":"INR"|"USD","item":"<lowercase singular noun phrase>","category":"<enum>","type":"expense"|"income"}]}

Categories: Food, Drinks, Groceries, Transport, Shopping, Entertainment, Bills, Health, Education, Personal, Gifts, Income, Other.

Rules:
- Currency defaults to INR. Use USD only when the input explicitly says "dollars" or contains "$".
- Amounts: "k" = ×1000, "hazaar" = ×1000, "sau" = ×100, "lakh" = ×100000. Convert number-words ("five hundred") to digits.
- type is "expense" by default; "income" only for explicit salary, cashback, refund, gift received, payment received.
- For disfluencies and corrections ("500 wait no 600"), output the CORRECTED amount only.
- For ambiguous items ("that thing", "stuff"), use item "unspecified" and category "Other".
- Item field: lowercase singular noun phrase ("uber ride", "beer", "chai" — not "Beers" or "Uber").
- Multi-transaction inputs become multiple array entries in spoken order.
- Category heuristics: uber/ola/auto/petrol/bus/metro → Transport; beer/wine/chai/coffee/juice → Drinks; rent/electricity/wifi/recharge/gas → Bills; movie/netflix/concert → Entertainment; doctor/medicine/hospital → Health.'''

llm = Llama(
    model_path="txn-parser-qwen3-0.6b-Q4_K_M.gguf",
    n_gpu_layers=-1, n_ctx=2048,
)
out = llm.create_chat_completion(
    messages=[
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user",   "content": "200 ka samosa"},
    ],
    temperature=0.0,
)
print(out["choices"][0]["message"]["content"])

Inference (CLI, llama.cpp)

./llama-cli -m txn-parser-qwen3-0.6b-Q4_K_M.gguf \
    --grammar-file scripts/grammar.gbnf \
    --system-prompt "$(cat system_prompt.txt)" \
    -p "200 ka samosa" -n 256

Reproduce

git clone https://github.com/kartikeychoudhary/txn-parser.git
cd txn-parser && bash setup.sh
python scripts/train_and_publish.py --only qwen3-0.6b

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Auto-published by scripts/train_and_publish.py on 2026-05-22T22:42:56.387243+00:00.