---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- pruned_flex_olmo
- custom_code
- math
- pruned
- distilled
- mixture-of-experts
base_model: allenai/Flex-math-2x7B-1T
pipeline_tag: text-generation
---

# flex-math-2048

A pruned and distilled variant of [allenai/Flex-math-2x7B-1T](https://huggingface.co/allenai/Flex-math-2x7B-1T) with a variable-width expert MLP. Expert 1 has been pruned from the full 11,008 intermediate size down to **2048** (19% of original width), then recovered via knowledge distillation.

| | |
|---|---|
| **Total Parameters** | 8.1B |
| **Expert 1 Parameters** | 0.8B |
| **Expert 1 Width** | 2048 (19%) |
| **Base Model** | allenai/Flex-math-2x7B-1T (11.6B params) |

For full details, see the [blog post](https://hbfreed.com/2026/01/28/variable-flexolmo.html).

## How to Use

This repo includes a `modeling_pruned_flex_olmo.py` file that handles the variable-width expert architecture. Just load with `trust_remote_code=True` and it works like any other HuggingFace model:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("hbfreed/flex-math-2048", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("allenai/Flex-math-2x7B-1T")

input_text = "Solve: What is 15% of 200?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

The tokenizer is the same as the base model's.

## How It Was Made

1. **Structured pruning**: Neuron importance scores were computed on math-specific data (GSM8k, Metamath, TuluMath subsets). The least important neurons in Expert 1's gate/up/down projections were removed, reducing intermediate size from 11,008 to 2048.
2. **Knowledge distillation**: The pruned model was retrained for ~228M tokens using the top-128 logprobs from the full-sized teacher model. Distillation data: [hbfreed/flexolmo-math-logprobs](https://huggingface.co/datasets/hbfreed/flexolmo-math-logprobs).

Math-calibrated importance analysis was used — 58% of the top-2048 neurons differ between math-calibrated and general-calibrated rankings.

## Benchmark Results

| Model | GSM8K | MATH | Math2 |
|---|---|---|---|
| No-expert baseline (7.3B) | — | — | 8.1 |
| **flex-math-2048** | **44.3** | **13.9** | **29.1** |
| Full teacher (11.6B) | 69.7 | 35.4 | 52.5 |

### All Variants

| Model | Total Params | Expert Width | GSM8K | MATH | Math2 |
|---|---|---|---|---|---|
| [flex-math-8192](https://huggingface.co/hbfreed/flex-math-8192) | 10.5B | 8192 (74%) | 70.1 | 31.3 | 50.7 |
| [flex-math-5504](https://huggingface.co/hbfreed/flex-math-5504) | 9.5B | 5504 (50%) | 66.6 | 26.8 | 46.7 |
| [flex-math-2048](https://huggingface.co/hbfreed/flex-math-2048) | 8.1B | 2048 (19%) | 44.3 | 13.9 | 29.1 |

## License

Apache 2.0 (same as base model)