Instructions to use finndot/finnai-slm-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use finndot/finnai-slm-v2 with Transformers:

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

pipe = pipeline("text-generation", model="finndot/finnai-slm-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("finndot/finnai-slm-v2")
model = AutoModelForCausalLM.from_pretrained("finndot/finnai-slm-v2", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use finndot/finnai-slm-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "finndot/finnai-slm-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "finndot/finnai-slm-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/finndot/finnai-slm-v2

SGLang

How to use finndot/finnai-slm-v2 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 "finndot/finnai-slm-v2" \
    --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": "finndot/finnai-slm-v2",
		"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 "finndot/finnai-slm-v2" \
        --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": "finndot/finnai-slm-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use finndot/finnai-slm-v2 with Docker Model Runner:
```
docker model run hf.co/finndot/finnai-slm-v2
```

FinnAI SLM v2

On-device model for Indian bank SMS → JSON and personal finance Q&A.

Useful when you want structured spends from bank SMS on the phone, without uploading messages to a cloud LLM.


Model	`finndot/finnai-slm-v2`
Dataset	`finndot/finnai-slm-data`
Base	`Qwen/Qwen3-1.7B`
License	Apache 2.0
App	FinnDot

FinnAI model family

Version	Weights	On-device LiteRT	Data
v4 (latest)	`finnai-slm-v4`	`finnai-slm-v4-litertlm`	`finnai-slm-data-v4`
v3	`finnai-slm-v3`	`finnai-slm-v3-litertlm`	`finnai-slm-data-v3`
v2 (this)	`finnai-slm-v2`	—	`finnai-slm-data`

Prefer v4 for new projects.

What it helps with

Capability	How it helps
SMS → JSON	Extracts amount, merchant, type, account, balance, category from Indian bank SMS
Noise refusal	Returns `{}` for OTP / promo / KYC instead of inventing spends
Finance coach	Short answers about spending, SIP, EMI, budgeting
Privacy	Designed for on-device expense tracking (FinnDot)

Quick start

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "finndot/finnai-slm-v2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, torch_dtype="bfloat16", device_map="auto"
)

messages = [
    {"role": "system", "content": "Extract transaction details from this SMS as JSON: {amount, merchant, type, account, balance, category}. type is one of: INCOME, EXPENSE, CREDIT, TRANSFER, INVESTMENT. If this is not a transaction (OTP, promo, KYC), return {}."},
    {"role": "user", "content": "HDFC Bank: Rs.499.00 debited from A/c XX1234 to SWIGGY via UPI. Avl Bal Rs.15000.50"}
]
text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True, enable_thinking=False
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=256, do_sample=False)
print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Quality snapshot

Metric	FinnAI v2	Untuned Qwen3-1.7B
Strict R-EM %	97.97	~28
Amount EM %	99.53	high but over-parses noise
JSON valid %	100.0	—

Limitations

Indian bank SMS focus; may not fit US/EU formats well
Merchant field is hardest; regex parsers can still win on supported banks
Smaller than general assistants — built for SMS + short finance chat

Citation

@misc{finnai-slm-v2,
  title={FinnAI SLM: On-device Indian bank SMS parsing},
  author={FinnDot Team},
  year={2026},
  url={https://huggingface.co/finndot/finnai-slm-v2}
}

License

Apache 2.0 — same as Qwen3-1.7B.

Downloads last month: 558

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for finndot/finnai-slm-v2

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Finetuned

(1348)

this model

Dataset used to train finndot/finnai-slm-v2

Evaluation results

Strict Record Exact-Match (R-EM) on FinnAI SMS test (synthetic)
self-reported

97.970
Amount Exact-Match on FinnAI SMS test (synthetic)
self-reported

99.530
JSON Validity on FinnAI SMS test (synthetic)
self-reported

100.000