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

# flex-general-2048

A pruned and partially 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 partially recovered via knowledge distillation.

Unlike the [math-calibrated variant](https://huggingface.co/hbfreed/flex-math-2048), this model's pruning was calibrated on **general-purpose data** — meaning importance scores were computed on a broad data mix rather than math-specific data. 58% of the top-2048 most important neurons differ between the two calibration approaches.

| | |
|---|---|
| **Total Parameters** | 8.1B |
| **Expert 1 Parameters** | 0.8B |
| **Expert 1 Width** | 2048 (19%) |
| **Base Model** | allenai/Flex-math-2x7B-1T (11.6B params) |
| **Distillation** | Partial (~20k steps, stopped early) |

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

inputs = tokenizer("Hello, world!", 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 general-purpose data. The least important neurons in Expert 1's gate/up/down projections were removed, reducing intermediate size from 11,008 to 2048.
2. **Partial knowledge distillation**: The pruned model was partially retrained (~20k steps) using logprobs from the full-sized teacher model. Training was stopped early — the general-calibrated model converged slower and to a higher loss than the math-calibrated variant.

## Related Models

| Model | Calibration | Expert Width | Distillation |
|---|---|---|---|
| [flex-math-8192](https://huggingface.co/hbfreed/flex-math-8192) | Math | 8192 (74%) | Full |
| [flex-math-5504](https://huggingface.co/hbfreed/flex-math-5504) | Math | 5504 (50%) | Full |
| [flex-math-2048](https://huggingface.co/hbfreed/flex-math-2048) | Math | 2048 (19%) | Full |
| **flex-general-2048** | **General** | **2048 (19%)** | **Partial** |

## License

Apache 2.0 (same as base model)