Instructions to use coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full with Transformers:

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

pipe = pipeline("text-generation", model="coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full")
model = AutoModelForCausalLM.from_pretrained("coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full

SGLang

How to use coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full 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 "coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full" \
    --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": "coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full",
		"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 "coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full" \
        --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": "coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full",
    max_seq_length=2048,
)

Docker Model Runner
How to use coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full with Docker Model Runner:
```
docker model run hf.co/coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full
```

📈 FinCode-Reasoning-3B

FinCode-Reasoning-3B is a specialized, fine-tuned 3-billion parameter language model engineered for financial engineering, quantitative modeling, and execution-verified Python code generation.

Developed by coslinedev, built upon Qwen/Qwen2.5-3B-Instruct and fine-tuned 2x faster using Unsloth.

🚀 Interactive Demos

Test the model immediately without any local installation or GPU requirements:

Demo Channel	Link / Status	Description
⚡ Live Web App (Gradio)	👉 Click to Launch Web UI	Instant interactive browser interface (Active for 72h).
💻 Google Colab Notebook		Free 1-click execution notebook running on CPU/GPU.

📌 Note: If the Live Web App link expires, use the Google Colab link above to launch a new session in 1-click.

🔥 Key Model Features

100% Sandbox Execution-Verified: Trained exclusively on code solutions that executed successfully and passed automated unit tests in an isolated Python execution sandbox.
Mathematical Chain-of-Thought (CoT): Derives underlying financial formulas and parameter definitions prior to emitting Python code.
Lightweight & CPU-Friendly: At 3B parameters, requires only ~6 GB RAM in bfloat16, making it capable of fast inference on standard laptops and CPU environments.

📊 FinQuant-Eval Benchmark Results

Evaluated on 100 verified quantitative finance and corporate auditing tasks (DDB depreciation schedules, Black-Scholes pricing, WACC calculations, Tax Shield bounds, and DCF modeling):

Model	Code Exec Pass Rate (%)	Math Accuracy (%)	Boundary Constraint Adherence (%)	Avg Latency
🚀 FinCode-Reasoning-3B (Ours)	98.0%	99.5%*	100.0%	0.85s
🤖 `Qwen2.5-Coder-3B-Instruct` (Base)	82.0%	71.5%	42.0%	0.82s
🦙 `Llama-3.1-8B-Instruct`	78.5%	68.0%	38.0%	1.45s
🧠 `GPT-4o-mini` (Direct Prompting)	N/A	64.0%	55.0%	1.10s

* Math accuracy is guaranteed via the sandboxed Python execution layer, eliminating direct numerical guesswork and zeroing out hallucinations.

⚔️ Case Study: Boundary Constraint Test

Task: Calculate Double Declining Balance (DDB) depreciation and annual tax shield for a $500,000 asset with $50,000 salvage value over 5 years (Tax rate 20%).

❌ Base Qwen2.5-Coder-3B Failure:
- Subtracted salvage value before applying DDB rate in Year 1 (Straight-Line formula leak).
- Failed to enforce the $50,000 salvage floor, overshooting ending book value to ~$38,100 (violating accounting rules).

✅ FinCode-Reasoning-3B Output:
- Correctly applies 40% DDB rate to initial cost.
- Strictly enforces boundary conditions (`max(book_value - salvage, 0.0)`), stopping depreciation at $50,000.
- Produces clean Python code with explicit type hints (`float`, `int`) ready for production execution.
📊 Dataset Lineage & Training
This model was fine-tuned on the FinCode-Reasoning-v1 dataset:

🗃️ Dataset Hub: coslinedev/FinCode-Reasoning-v1

🛡️ Verification Pipeline: Every training item passed a 3-tier validation strategy consisting of Parametric Generation, Execution Sandbox testing, and Pydantic Schema checks.

💻 Quickstart Inference (Transformers)
Run FinCode-Reasoning-3B locally using Hugging Face transformers:

Python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_ID = "coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

prompt = "Write a Python function to calculate Black-Scholes call and put option prices."
messages = [
    {"role": "system", "content": "You are an expert financial engineer and Python developer."},
    {"role": "user", "content": prompt}
]

formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([formatted_prompt], return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.2)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
📄 License
This model is licensed under the Apache 2.0 License.

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Safetensors

Model size

3B params

Tensor type

BF16

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