Instructions to use 4-alokk/flashflow-qwen3-0.6b-bank-sms with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 4-alokk/flashflow-qwen3-0.6b-bank-sms with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B")
model = PeftModel.from_pretrained(base_model, "4-alokk/flashflow-qwen3-0.6b-bank-sms")

Transformers

How to use 4-alokk/flashflow-qwen3-0.6b-bank-sms with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="4-alokk/flashflow-qwen3-0.6b-bank-sms")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("4-alokk/flashflow-qwen3-0.6b-bank-sms", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use 4-alokk/flashflow-qwen3-0.6b-bank-sms with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "4-alokk/flashflow-qwen3-0.6b-bank-sms"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "4-alokk/flashflow-qwen3-0.6b-bank-sms",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/4-alokk/flashflow-qwen3-0.6b-bank-sms

SGLang

How to use 4-alokk/flashflow-qwen3-0.6b-bank-sms with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "4-alokk/flashflow-qwen3-0.6b-bank-sms" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "4-alokk/flashflow-qwen3-0.6b-bank-sms",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "4-alokk/flashflow-qwen3-0.6b-bank-sms" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "4-alokk/flashflow-qwen3-0.6b-bank-sms",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use 4-alokk/flashflow-qwen3-0.6b-bank-sms with Docker Model Runner:
```
docker model run hf.co/4-alokk/flashflow-qwen3-0.6b-bank-sms
```

FlashFlow Qwen3-0.6B — Bank SMS Structured Extractor

A LoRA adapter fine-tuned from Qwen/Qwen3-0.6B to parse Indian bank transaction SMS into structured JSON. Given a raw SMS string, the model outputs a JSON object with fields: transaction_type, amount, currency, date, time, sender_bank, sender_acc, receiver_bank, receiver_acc, counterparty_name, reference_id, balance_after, is_actionable.

Model Details

Base model: Qwen/Qwen3-0.6B
Method: LoRA (PEFT), r=16, alpha=32, dropout=0.05
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Task: Structured JSON extraction from Indian bank SMS text
Training data: 4-alokk/flashflow-bank-sms — 15,000 synthetic training examples, 200 held-out eval examples

How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_id = "Qwen/Qwen3-0.6B"
adapter_id = "4-alokk/flashflow-qwen3-0.6b-bank-sms"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)
model = AutoModelForCausalLM.from_pretrained(base_model_id)
model = PeftModel.from_pretrained(model, adapter_id)

sms = "Your account 5986 has been debited by Rs.22534.00 on 17/03/24 at More Supermarket. Net Bal: Rs 48,009."
instruction = (
    "You are a financial data extractor. Parse the bank SMS and return a JSON object with these fields: "
    "transaction_type, amount, currency, date (ISO 8601), time, sender_bank, sender_acc, receiver_bank, "
    "receiver_acc, counterparty_name, reference_id, balance_after, is_actionable. Use null for absent fields."
)

messages = [{"role": "user", "content": f"{instruction}\n\n{sms}"}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
output = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(output[0][inputs.shape[1]:], skip_special_tokens=True))

Training Procedure

Trained with TRL SFTTrainer on 15,000 synthetic Indian bank SMS examples (40% debit, 40% credit, 20% non-actionable/noise), 1500 steps.

Training Hyperparameters

max_steps: 1500
per_device_train_batch_size: 2
gradient_accumulation_steps: 8
learning_rate: 1.5e-4
lr_scheduler_type: cosine
warmup_steps: 150
max_seq_length: 512
LoRA: r=16, alpha=32, dropout=0.05

Framework versions

PEFT 0.19.1
TRL: 1.3.0
Transformers: 5.8.0
Pytorch: 2.11.0
Datasets: 4.8.5
Tokenizers: 0.22.2

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