Instructions to use finndot/finnai-slm-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use finndot/finnai-slm-v3 with Transformers:

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

pipe = pipeline("text-generation", model="finndot/finnai-slm-v3")
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-v3")
model = AutoModelForCausalLM.from_pretrained("finndot/finnai-slm-v3", 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-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "finndot/finnai-slm-v3"
# 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-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/finndot/finnai-slm-v3

SGLang

How to use finndot/finnai-slm-v3 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-v3" \
    --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-v3",
		"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-v3" \
        --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-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use finndot/finnai-slm-v3 with Docker Model Runner:
```
docker model run hf.co/finndot/finnai-slm-v3
```

FinnAI SLM v3

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

Helps expense apps extract transactions from bank SMS privately on the device, and answer simple money questions without cloud LLM calls.


Model	`finndot/finnai-slm-v3`
On-device INT4	`finndot/finnai-slm-v3-litertlm`
Dataset	`finndot/finnai-slm-data-v3`
Base	`Qwen/Qwen3-1.7B`
License	Apache 2.0

FinnAI model family

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

For new work, prefer v4 (more banks, harder SMS, Learn/Analytics chat).

What it helps with

Parse Indian bank SMS into structured JSON (amount, merchant, type, …)
Ignore OTP / promo / KYC ({})
Short ledger-grounded finance answers
Run on-device via the LiteRT INT4 package

Quick start

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "finndot/finnai-slm-v3"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, torch_dtype="bfloat16", device_map="auto"
)

Use enable_thinking=False in the chat template for on-device-style decoding.

Limitations

Optimized for Indian banking SMS; not a general chatbot. See v4 for the latest quality and coverage.

License

Apache 2.0

Downloads last month: 615

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for finndot/finnai-slm-v3

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Finetuned

(1349)

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Finetunes

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