TroglodyteDerivations/qwen2p5-math-1p5b-merged

create_model_card.py

import torch

model_card = """--- language: - en tags: - math - reasoning - qwen - symbolic-math - algebra - calculus license: apache-2.0 datasets: - mathematical-reasoning - symbolic-math

Qwen2.5-Math-1.5B with Enhanced Symbolic Math Capabilities

Model Description

This is a merged version of Qwen2.5-Math-1.5B with the Kevinmastascusa/symbolic-math-qwen2p5-1p5b-lora adapter, providing enhanced symbolic mathematical reasoning capabilities.

Model Sources

• Base Model: Qwen/Qwen2.5-Math-1.5B
• LoRA Adapter: Kevinmastascusa/symbolic-math-qwen2p5-1p5b-lora
• Merged By: TroglodyteDerivations

Capabilities

• ✅ Algebraic problem solving
• ✅ Step-by-step mathematical reasoning
• ✅ Symbolic math operations
• ✅ Calculus and derivatives
• ✅ Percentage calculations
• ✅ Quadratic equations
• ✅ Geometric calculations

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "TroglodyteDerivations/qwen2p5-math-1p5b-merged"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True,
    torch_dtype=torch.float32,
    device_map="auto"
)

# Solve a math problem
prompt = "Solve for x: 2x + 5 = 13"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=150,
        do_sample=False,
        pad_token_id=tokenizer.eos_token_id
    )

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