Instructions to use finndot/finnai-slm-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use finndot/finnai-slm-v4 with Transformers:

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

pipe = pipeline("text-generation", model="finndot/finnai-slm-v4")
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-v4")
model = AutoModelForCausalLM.from_pretrained("finndot/finnai-slm-v4", 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]:]))

LiteRT

How to use finndot/finnai-slm-v4 with LiteRT:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

LiteRT-LM

How to use finndot/finnai-slm-v4 with LiteRT-LM:

# LiteRT-LM runs on various platforms (Android, iOS, Windows, Linux, macOS, IoT, Web/WASM)
# and supports many APIs (C++, Python, Kotlin, Swift, JavaScript, Flutter).
# For platform-specific integration guides, please refer to the official developer website:
# https://ai.google.dev/edge/litert-lm

# To try LiteRT-LM, the easiest way is to use our CLI tool.
# 1. Install the LiteRT-LM CLI tool:
pip install -U litert-lm

# 2. Download and run this model locally:
# See: https://ai.google.dev/edge/litert-lm/cli
# A single .litertlm file in the repo is picked automatically; otherwise the CLI asks which one to run
# (or pass its name right after the repo id).
litert-lm run \
  --from-huggingface-repo=finndot/finnai-slm-v4 \
  --prompt="Write me a poem"

PEFT
How to use finndot/finnai-slm-v4 with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use finndot/finnai-slm-v4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "finndot/finnai-slm-v4"
# 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-v4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/finndot/finnai-slm-v4

SGLang

How to use finndot/finnai-slm-v4 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-v4" \
    --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-v4",
		"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-v4" \
        --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-v4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use finndot/finnai-slm-v4 with Docker Model Runner:
```
docker model run hf.co/finndot/finnai-slm-v4
```

FinnAI SLM v4

On-device language model for Indian bank SMS → JSON and personal finance coaching.

Use this model when you need private, phone-friendly understanding of Indian banking SMS and short finance answers — without sending user messages to the cloud.


Model	`finndot/finnai-slm-v4`
On-device INT4	`finndot/finnai-slm-v4-litertlm`
Dataset	`finndot/finnai-slm-data-v4`
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	`finnai-slm-v2`	—	`finnai-slm-data`

Prefer v4 for new apps and fine-tunes: broader SMS coverage, Ask / Learn / Analytics chat aligned with FinnDot.

What it helps with

Capability	How it helps
SMS → JSON	Turns UPI / NEFT / IMPS / card / salary / ATM SMS into `{amount, merchant, type, account, balance, category}`
Noise refusal	Returns `{}` for OTP, promo, KYC — avoids fake transactions
Ask	Short answers grounded in a spending ledger
Learn	Tutor-style explanations (SIP, EMI, tax, budgeting)
Analytics	Spending tips without inventing numbers
On-device	INT4 LiteRT build runs on Android; user SMS stays on the phone

Languages: English, Hindi, Hinglish, and other Indic bank SMS styles in the training mix.

Quick start

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "finndot/finnai-slm-v4"
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)
out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

LoRA adapter: adapter/ in this repo (base: finndot/finnai-slm-v3).

Quality snapshot (held-out test)

Metric	FinnAI v4	Untuned Qwen3-1.7B
SMS R-EM %	94.87	29.39
Amount EM %	99.7	78.75
JSON valid %	100.0	100.0
Chat groundedness %	80.77	47.44

Full report: eval/v20261005-finnai-slm-v4.md.

On-device file

For Android / LiteRT-LM, use finndot/finnai-slm-v4-litertlm (~929 MB .litertlm).

Limitations

Best on Indian banking SMS; weaker on unfamiliar foreign formats.
Merchant names can still be hard; bank-specific regex parsers may win when available.
1.7B — specialized for SMS + finance chat, not a general assistant.
Thinking / long chain-of-thought is disabled for on-device use.

Citation

@misc{finnai-slm-v4,
  title={FinnAI SLM v4: On-device Indian bank SMS parsing and finance coaching},
  author={FinnDot Team},
  year={2026},
  url={https://huggingface.co/finndot/finnai-slm-v4}
}

License

Apache 2.0 — same as Qwen3-1.7B.

Downloads last month: 649

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for finndot/finnai-slm-v4

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Finetuned

(1357)

this model

Adapters

1 model

Finetunes

2 models

Dataset used to train finndot/finnai-slm-v4

Evaluation results

Strict Record Exact-Match (R-EM) on FinnAI v4 SMS test (synthetic, 2028)
self-reported

94.870
Amount Exact-Match on FinnAI v4 SMS test (synthetic, 2028)
self-reported

99.700
JSON Validity on FinnAI v4 SMS test (synthetic, 2028)
self-reported

100.000