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Gaurav8HF
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Model Details
Model Provider
Gaurav8HF
Model Tree
Qwen/Qwen3-0.6B
Input Modalities
Output Modalities
Supported Functionality
json
{"intent": "<one of 20 intents>", "entity": "<string or null>", "amount": "<number or null>", "account": "checking | savings | credit | investment | null"}
Intents: check_balance, transfer_funds, pay_bill, dispute_transaction, freeze_card,
unfreeze_card, report_lost_card, request_new_card, view_transaction_history,
set_spending_alert, update_credit_limit, apply_for_loan, check_loan_status, buy_stock,
sell_stock, check_portfolio, schedule_recurring_payment, cancel_recurring_payment,
open_account, close_account.
| LoRA rank / alpha | 8 / 16 |
| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Trainable params | 5,046,272 / 601,096,192 (0.84%) |
| Epochs | 3 |
| Effective batch size | 16 (batch 4 × grad-accum 4) |
| Learning rate | 2e-4, cosine schedule, 3% warmup |
| Hardware | CPU only, float32 |
| Data | 450 train / 60 val, synthetic, 20 intents, roughly balanced |
| Epoch | eval_loss |
|---|---|
| 1 | 0.0789 |
| 2 | 0.0265 |
| 3 (final) | 0.0214 |
The eval loss above is measured on validation examples generated by the same synthetic process as training, so it mostly reflects whether the model learned the output format. Testing separately on 10 hand-written prompts with fresh wording/entities not in train or val told a more honest story:
intent value that isn't in the 20-label taxonomy at
all (e.g. lock_renewable_document instead of freeze_card for "lock my visa, someone jacked
it"; check_revenue instead of check_balance for "how much runway do I have left in
checking").Solid for cleanly-phrased commands close to the training distribution; not yet reliable enough to
trust blindly on informal or unusual phrasing. Validate intent against the known list of 20
before acting on it downstream.
python
import torchfrom peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerBASE_MODEL = "Qwen/Qwen3-0.6B"ADAPTER = "Gaurav8HF/Fintech-Fine-Tune"tokenizer = AutoTokenizer.from_pretrained(ADAPTER)base_model = AutoModelForCausalLM.from_pretrained(BASE_MODEL, dtype=torch.float32)model = PeftModel.from_pretrained(base_model, ADAPTER)messages = [{"role": "user", "content": "freeze my card, I think I lost it"}]prompt_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=False,enable_thinking=False, # must match training -- Qwen3 normally "thinks" before answering)out = model.generate(prompt_ids, max_new_tokens=64, do_sample=False, pad_token_id=tokenizer.pad_token_id)print(tokenizer.decode(out[0, prompt_ids.shape[1]:], skip_special_tokens=True))