---
library_name: transformers
tags: []
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
Classification model finetuned for prompt-model routing based on code prompt difficulty.

## Model Details

Base model: answerdotai/ModernBERT-base

### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** [Christian @ Prime Intellect]
- **Finetuned from model:** [answerdotai/ModernBERT-base]


## How to Get Started with the Model

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
```
from transformers import pipeline
import torch

# Load the model
classifier = pipeline(
    "text-classification",
    model="cdreetz/modern-bert-router",
    device=0 if torch.cuda.is_available() else -1
)

# Test easy problem
easy_problem = """
Write a function that returns the sum of two numbers.

Example:
Input: add(2, 3)
Output: 5
"""

# Test hard problem
hard_problem = """
Given a binary tree, find the maximum path sum. The path may start and end at any node in the tree.
A path is defined as any sequence of nodes from some starting node to any node in the tree along the parent-child connections.
The path must contain at least one node and does not need to go through the root.

Example:
Input: root = [1,2,3]
Output: 6 (2 -> 1 -> 3)
"""

# Run predictions
result_easy = classifier(easy_problem)[0]
result_hard = classifier(hard_problem)[0]

print("Easy Problem:")
print(f"  Difficulty: {result_easy['label']}")
print(f"  Confidence: {result_easy['score']:.2%}\n")

print("Hard Problem:")
print(f"  Difficulty: {result_hard['label']}")
print(f"  Confidence: {result_hard['score']:.2%}")
```


## Training Details

```
# /// script
# dependencies = [
#   "chatan",
#   "transformers",
#   "datasets",
#   "torch",
#   "accelerate",
#   "scikit-learn",
#   "triton",
#   "huggingface_hub"
# ]
# ///

import os
import asyncio
import chatan as ch
from datasets import Dataset as hf_ds
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
import numpy as np
from sklearn.metrics import accuracy_score, f1_score
import torch
import triton
from huggingface_hub import login

#torch._dynamo.config.suppress_errors = True

login()

async def create_dataset():
    gen = ch.async_generator("openai", os.getenv("OPENAI_API_KEY"))

    ds = ch.async_dataset({
        "difficulty": ch.sample.choice(["easy", "hard"]),
        "text": gen("write a coding problem of difficulty {difficulty}")
    })

    df = await ds.generate(n=1000, max_concurrent_rows=500)
    df['labels'] = df['difficulty'].map({"easy": 0, "hard": 1})
    dataset = hf_ds.from_pandas(df[['text', 'labels']])
    return dataset.train_test_split(test_size=0.2, seed=42)


def train(dataset):
    tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base")
    model = AutoModelForSequenceClassification.from_pretrained(
        "answerdotai/ModernBERT-base",
        num_labels=2,
        label2id={"easy": 0, "hard": 1},
        id2label={0: "easy", 1: "hard"},
        problem_type="single_label_classification"
    )


    def tokenize(examples):
        return tokenizer(
            examples['text'], 
            padding='max_length', 
            truncation=True, 
            max_length=512
        )

    tokenized = dataset.map(tokenize, batched=True, remove_columns=['text'])
    tokenized.set_format('torch')

    training_args = TrainingArguments(
        output_dir="./modernbert-router",
        eval_strategy="epoch",
        num_train_epochs=3,
        per_device_train_batch_size=16,
        learning_rate=2e-5,
        save_strategy="epoch",
        load_best_model_at_end=True
    )

    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized["train"],
        eval_dataset=tokenized["test"],
        processing_class=tokenizer,
        compute_metrics=lambda p: {
            "accuracy": accuracy_score(p.label_ids, np.argmax(p.predictions, axis=1)),
            "f1": f1_score(p.label_ids, np.argmax(p.predictions, axis=1))
        }
    )

    print("starting training")
    trainer.train()

    model.save_pretrained("./modernbert-router")
    tokenizer.save_pretrained("./modernbert-router")

    print("donezo")

    #model = AutoModelForSequenceClassification.from_pretrained("./modernbert-router")
    #tokenizer = AutoTokenizer.from_pretrained("./modernbert-router")

    #model.push_to_hub("cdreetz/modern-bert-router")
    #tokenizer.push_to_hub("cdreetz/modern-bert-router")



if __name__ == "__main__":
    dataset = asyncio.run(create_dataset())
    train(dataset)
```




## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**
```
@software{modern-bert-router,
  author = {Reetz, Christian},
  title = {ModernBERTRouter},
  url = {https://huggingface.co/cdreetz/modern-bert-router/},
  year = {2025}
}
```