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
```

Qwen2.5-3B-FinCode-Reasoning-Full / README.md

coslinedev

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	---
	license: apache-2.0
	language:
	- en
	pipeline_tag: text-generation
	tags:
	- financial-modeling
	- fintech
	- quant
	- python
	- code-generation
	- reasoning
	- chain-of-thought
	- execution-verified
	- unsloth
	base_model: Qwen/Qwen2.5-3B-Instruct
	pretty_name: FinCode-Reasoning-3B
	library_name: transformers
	---

	# 📈 FinCode-Reasoning-3B

	[![License](https://img.shields.io/badge/License-Apache_2.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
	[![Hugging Face Dataset](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-FinCode--Reasoning--v1-blue)](https://huggingface.co/datasets/coslinedev/FinCode-Reasoning-v1)
	[![Unsloth](https://img.shields.io/badge/Powered%20by-Unsloth-FF69B4)](https://github.com/unslothai/unsloth)

	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](https://github.com/unslothai/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](https://0f0743a77ec537bffa.gradio.live/) \| Instant interactive browser interface (Active for 72h). \|
	\| 💻 Google Colab Notebook \| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/11TXF91zmoPnDkSRcepBJRR8OZINPMoGj?usp=sharing) \| 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%).

	```text
	❌ 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.