Instructions to use siddhartha-addy-globalids-labs/qwen3.5-2b-finance-alpaca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use siddhartha-addy-globalids-labs/qwen3.5-2b-finance-alpaca with Transformers:

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

pipe = pipeline("text-generation", model="siddhartha-addy-globalids-labs/qwen3.5-2b-finance-alpaca")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("siddhartha-addy-globalids-labs/qwen3.5-2b-finance-alpaca")
model = AutoModelForMultimodalLM.from_pretrained("siddhartha-addy-globalids-labs/qwen3.5-2b-finance-alpaca", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use siddhartha-addy-globalids-labs/qwen3.5-2b-finance-alpaca with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use siddhartha-addy-globalids-labs/qwen3.5-2b-finance-alpaca with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "siddhartha-addy-globalids-labs/qwen3.5-2b-finance-alpaca"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "siddhartha-addy-globalids-labs/qwen3.5-2b-finance-alpaca",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/siddhartha-addy-globalids-labs/qwen3.5-2b-finance-alpaca

SGLang

How to use siddhartha-addy-globalids-labs/qwen3.5-2b-finance-alpaca 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 "siddhartha-addy-globalids-labs/qwen3.5-2b-finance-alpaca" \
    --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": "siddhartha-addy-globalids-labs/qwen3.5-2b-finance-alpaca",
		"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 "siddhartha-addy-globalids-labs/qwen3.5-2b-finance-alpaca" \
        --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": "siddhartha-addy-globalids-labs/qwen3.5-2b-finance-alpaca",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use siddhartha-addy-globalids-labs/qwen3.5-2b-finance-alpaca with Docker Model Runner:
```
docker model run hf.co/siddhartha-addy-globalids-labs/qwen3.5-2b-finance-alpaca
```

Qwen 3.5 2B Finance Alpaca LoRA

A LoRA fine-tuned version of Qwen 3.5 2B trained on the Finance-Alpaca dataset for finance-oriented instruction following and question answering.

This model was created as an experiment to explore parameter-efficient fine-tuning (PEFT) of a relatively small language model for financial-domain tasks.

Model Details

Property	Details
Base Model	Qwen 3.5 2B
Fine-tuning Method	LoRA
Training Dataset	Finance-Alpaca
Epochs	1
Training Framework	Unsloth
Model Type	Causal Language Model
Domain	Finance
Language	English

All training parameters use the default configuration provided by the training setup, with the number of training epochs explicitly set to 1.

Intended Use

This model is intended primarily for:

Research and experimentation
Learning and demonstrating LoRA fine-tuning
Finance-related question answering
Financial instruction-following experiments
Evaluating domain adaptation of small language models
Experimenting with parameter-efficient fine-tuning

This model is not intended to provide professional financial advice.

Training

The model was fine-tuned using Low-Rank Adaptation (LoRA).

Unlike full fine-tuning, LoRA does not update all parameters of the base model. Instead, it introduces a relatively small number of trainable parameters that learn the desired task or domain adaptation.

The training dataset used was Finance-Alpaca, which contains instruction-response examples related to financial topics.

Training Configuration

The training configuration used the default values from the training environment, with the following explicit modification:

Base model: Qwen 3.5 2B
Dataset: Finance-Alpaca
Fine-tuning method: LoRA
Epochs: 1
Other parameters: Default

Usage

Using the LoRA Adapter

If this repository contains the LoRA adapter, the original base model must be loaded first and the adapter applied on top of it.

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = "Qwen/Qwen3.5-2B"
adapter_model = "siddhartha-addy-globalids-labs/qwen3.5-2b-finance-alpaca-lora"

tokenizer = AutoTokenizer.from_pretrained(base_model)

model = AutoModelForCausalLM.from_pretrained(
    base_model,
    device_map="auto"
)

model = PeftModel.from_pretrained(
    model,
    adapter_model
)

prompt = "Explain the difference between a stock and a bond."

inputs = tokenizer(
    prompt,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=256
)

response = tokenizer.decode(
    outputs[0],
    skip_special_tokens=True
)

print(response)

Using with PEFT

The adapter can also be loaded using the PEFT library:

from peft import AutoPeftModelForCausalLM

model = AutoPeftModelForCausalLM.from_pretrained(
    "siddhartha-addy-globalids-labs/qwen3.5-2b-finance-alpaca-lora",
    device_map="auto"
)

Dataset

The model was trained on the Finance-Alpaca dataset.

The dataset consists of instruction-response examples covering financial concepts and questions.

Users should review the original dataset's license and terms before using this model or creating derivatives.

Limitations

This model has several important limitations:

It may generate incorrect financial information.
It may produce plausible-sounding but factually incorrect answers.
It does not have access to real-time market or financial data.
Its knowledge is limited by the base model and fine-tuning dataset.
One epoch of fine-tuning does not guarantee optimal performance.
The model has not been professionally validated for financial applications.
It should not be used as the sole source of information for investment or financial decisions.

Financial Disclaimer

This model is not a financial advisor.

Outputs generated by this model should not be interpreted as investment, trading, tax, legal, accounting, or other professional financial advice.

Always verify financial information with reliable sources and consult a qualified professional before making financial decisions.

Acknowledgements

This work builds upon the following projects and resources:

Qwen for the base language model.
Finance-Alpaca for the financial instruction dataset.
Unsloth for the efficient fine-tuning workflow.
Hugging Face for the model hosting and open-source ML ecosystem.
PEFT for parameter-efficient fine-tuning.

License

The licensing of this model is subject to the licensing terms of the underlying Qwen 3.5 2B model and the Finance-Alpaca dataset.

Please review the original licenses before using or redistributing this model.

Citation

If you use this model in your research or experiments, please reference the original Qwen model, Finance-Alpaca dataset, and the relevant fine-tuning framework.

Organization: siddhartha-addy-globalids-labs

Model: qwen3.5-2b-finance-alpaca-lora

Fine-tuning: LoRA

Epochs: 1

Downloads last month: 116

Safetensors

Model size

2B params

Tensor type

BF16

F32

Model tree for siddhartha-addy-globalids-labs/qwen3.5-2b-finance-alpaca

Base model

Qwen/Qwen3.5-2B-Base

Finetuned

Qwen/Qwen3.5-2B

Adapter

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