Instructions to use Rumiii/Muse-Glimmer-30B-FinanceIND with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rumiii/Muse-Glimmer-30B-FinanceIND with Transformers:

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

pipe = pipeline("text-generation", model="Rumiii/Muse-Glimmer-30B-FinanceIND")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Rumiii/Muse-Glimmer-30B-FinanceIND")
model = AutoModelForMultimodalLM.from_pretrained("Rumiii/Muse-Glimmer-30B-FinanceIND", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Rumiii/Muse-Glimmer-30B-FinanceIND with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Rumiii/Muse-Glimmer-30B-FinanceIND"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rumiii/Muse-Glimmer-30B-FinanceIND",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Rumiii/Muse-Glimmer-30B-FinanceIND

SGLang

How to use Rumiii/Muse-Glimmer-30B-FinanceIND 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 "Rumiii/Muse-Glimmer-30B-FinanceIND" \
    --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": "Rumiii/Muse-Glimmer-30B-FinanceIND",
		"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 "Rumiii/Muse-Glimmer-30B-FinanceIND" \
        --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": "Rumiii/Muse-Glimmer-30B-FinanceIND",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Rumiii/Muse-Glimmer-30B-FinanceIND with Docker Model Runner:
```
docker model run hf.co/Rumiii/Muse-Glimmer-30B-FinanceIND
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Muse-Glimmer-30B-FinanceIND

An Indian personal-finance assistant fine-tuned from Meta's open Muse Glimmer 30B. It helps with SIPs, tax planning (old vs new regime, 80C, 80D, HRA), loans and EMIs, emergency funds and goal planning, calls calculation tools, and retries when a tool call fails.

What it can do

Greets users, explains what it can do, and asks clarifying questions when details are missing
Personalizes advice when a user profile is provided in the system prompt (or asks for one when it isn't)
Uses tools for exact calculations and recovers from tool errors
Declines tax evasion and guaranteed-return requests, and says when it has no live market data

Training

Method: QLoRA (r=32, alpha=32), 1 epoch, no truncation, then merged into BF16 weights
Data: Gandalf1/indian-finance-synthetic-phase2-cleaned (synthetic, quality-filtered), plus generated conversation, identity and tool-failure/retry examples
Loss on assistant turns only

Usage

The BF16 weights are ~55 GB. On a 24-40 GB GPU, load in 4-bit:

from unsloth import FastModel
model, tok = FastModel.from_pretrained("Rumiii/Muse-Glimmer-30B-FinanceIND", max_seq_length=8192, load_in_4bit=True)
FastModel.for_inference(model)

Recommended sampling: temperature 1.0, top_p 0.95, top_k 64.

Limitations

Tax rules in the training data follow FY 2024-25. India's Income-tax Act, 2025 applies from 1 April 2026, so always verify current rules and figures.
Educational guidance only, not regulated investment or tax advice.
It has no live market data and does not remember users between chats unless the app passes their profile.
Synthetic training data can contain errors; check important numbers.

License: Apache 2.0 (same as the base model).

Downloads last month: 124

Safetensors

Model size

30B params

Tensor type

BF16

Model tree for Rumiii/Muse-Glimmer-30B-FinanceIND

Base model

meta-models/Muse-Glimmer-30B

Finetuned

(68)

this model

Quantizations

2 models

Rumiii
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Muse-Glimmer-30B-FinanceIND