Instructions to use ApexVOrteX-1/Financial-Qwen-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ApexVOrteX-1/Financial-Qwen-Model with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "ApexVOrteX-1/Financial-Qwen-Model")

Transformers

How to use ApexVOrteX-1/Financial-Qwen-Model with Transformers:

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

pipe = pipeline("text-generation", model="ApexVOrteX-1/Financial-Qwen-Model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ApexVOrteX-1/Financial-Qwen-Model", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ApexVOrteX-1/Financial-Qwen-Model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ApexVOrteX-1/Financial-Qwen-Model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ApexVOrteX-1/Financial-Qwen-Model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ApexVOrteX-1/Financial-Qwen-Model

SGLang

How to use ApexVOrteX-1/Financial-Qwen-Model 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 "ApexVOrteX-1/Financial-Qwen-Model" \
    --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": "ApexVOrteX-1/Financial-Qwen-Model",
		"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 "ApexVOrteX-1/Financial-Qwen-Model" \
        --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": "ApexVOrteX-1/Financial-Qwen-Model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ApexVOrteX-1/Financial-Qwen-Model with Docker Model Runner:
```
docker model run hf.co/ApexVOrteX-1/Financial-Qwen-Model
```

Financial Analyst AI Assistant

A domain-specific Large Language Model adapter fine-tuned for financial question answering and reasoning tasks.

This model is a LoRA adapter built on top of Qwen2.5-1.5B-Instruct and trained using QLoRA with 4-bit quantization to efficiently adapt the model for financial analysis tasks.

Model Details

Model Description

Developed by: Ahmed Elsayed Taha
Model type: Causal Language Model (LLM) LoRA Adapter
Base model: Qwen2.5-1.5B-Instruct
Fine-tuning method: QLoRA (LoRA + 4-bit Quantization)
Language(s): English
License: Apache 2.0 (inherits from base model license)
Finetuned from: Qwen/Qwen2.5-1.5B-Instruct

Model Sources

Base Model: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct

Uses

Direct Use

This model can be used for:

Financial question answering
Accounting concept explanation
Financial reasoning tasks
Understanding financial contexts
Generating structured financial responses

Downstream Use

Possible applications:

Financial analyst assistants
Document-based financial analysis systems
Financial education tools
Retrieval Augmented Generation (RAG) pipelines
Business intelligence assistants

Out-of-Scope Use

This model should not be used as:

A replacement for professional financial advisors
A source of guaranteed investment decisions
A system for high-stakes financial decisions without human verification

Training Details

Training Data

The model was fine-tuned using:

TheFinAI/Fino1_Reasoning_Path_FinQA
TheFinAI/Fino1_Reasoning_Path_FinQA_v2

Dataset statistics:

Dataset	Samples
Fino1_Reasoning_Path_FinQA	5,499
Fino1_Reasoning_Path_FinQA_v2	3,472
Total	8,971

The dataset contains:

Open-ended financial questions
Ground-truth answers
Financial reasoning paths
Generated responses

Preprocessing

The dataset was converted into Qwen instruction format:

{
  "messages": [
    {
      "role": "system",
      "content": "You are a financial analyst AI assistant."
    },
    {
      "role": "user",
      "content": "Financial question"
    },
    {
      "role": "assistant",
      "content": "Financial answer"
    }
  ]
}

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