---
library_name: transformers
tags:
- compression
- expert-merging
- moe
- heretic
- uncensored
- decensored
- abliterated
license: apache-2.0
base_model:
- SamsungSAILMontreal/Qwen3-30B-A3B-Instruct-2507-REAM
---
# This is a decensored version of [SamsungSAILMontreal/Qwen3-30B-A3B-Instruct-2507-REAM](https://huggingface.co/SamsungSAILMontreal/Qwen3-30B-A3B-Instruct-2507-REAM), made using [Heretic](https://github.com/p-e-w/heretic) v1.2.0

## Abliteration parameters

| Parameter | Value |
| :-------- | :---: |
| **direction_index** | 23.04 |
| **attn.o_proj.max_weight** | 1.28 |
| **attn.o_proj.max_weight_position** | 31.26 |
| **attn.o_proj.min_weight** | 0.96 |
| **attn.o_proj.min_weight_distance** | 27.39 |
| **mlp.down_proj.max_weight** | 1.49 |
| **mlp.down_proj.max_weight_position** | 28.67 |
| **mlp.down_proj.min_weight** | 0.53 |
| **mlp.down_proj.min_weight_distance** | 26.87 |

## Performance

| Metric | This model | Original model ([SamsungSAILMontreal/Qwen3-30B-A3B-Instruct-2507-REAM](https://huggingface.co/SamsungSAILMontreal/Qwen3-30B-A3B-Instruct-2507-REAM)) |
| :----- | :--------: | :---------------------------: |
| **KL divergence** | 0.1994 | 0 *(by definition)* |
| **Refusals** | 2/100 | 100/100 |

-----


# Qwen3-30B-A3B-Instruct-2507-REAM

This model is a compressed version of [Qwen/Qwen3-30B-A3B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507). 
It is obtained by reducing the number of experts in each MoE layer from 128 to 96.
This reduction is achieved by the REAM method described in https://bknyaz.github.io/blog/2026/moe/.
The compressed model has 23B params (44GB) instead of 31B (57GB) of the original model, 
reducing storage and GPU memory requirements by roughly 25%. At the same time, 
the model retains >=94% of the original model's performance on a variety of benchmarks (see Evaluation section below).
Additional efficiency optimization (e.g., quantization) can be added similarly to the original model.

## Model Details

The model is exactly the same as Qwen/Qwen3-30B-A3B-Instruct-2507 except that number of experts is reduced from 128 to 96.

## Evaluation

Model is evaluated using https://github.com/EleutherAI/lm-evaluation-harness/ except for LiveCodeBench, which is evaluated using https://github.com/LiveCodeBench/LiveCodeBench.

The following versions were used for eval:

- python >= 3.10
- torch               : 2.7.1+cu126
- lm_eval             : 0.4.9.1
- vllm                : 0.10.1.1
- transformers        : 4.57.1
- datasets            : 3.2.0
- numpy               : 1.26.4

For tasks IFEval, AIME25, GSM8K and HumanEval the following command was used for eval on 4xNVIDIA H100:
`python -m lm_eval --model vllm --model_args pretrained=${model},tensor_parallel_size=4,dtype=auto,gpu_memory_utilization=0.9,data_parallel_size=1,max_model_len=131072 --tasks ${task} --batch_size 1 --apply_chat_template=True --confirm_run_unsafe_code`

For HumanEval, we use `--task=humaneval_instruct`.

For GPQA-Diamond, we add flags: `--num_fewshot 5 --fewshot_as_multiturn` and set `--task=gpqa_diamond_n_shot`.

For LiveCodeBench, we evaluate using:
`python -m lcb_runner.runner.main --model Qwen/Qwen3-30B-A3B-Instruct-2507 --scenario codegeneration --evaluate --local_model_path ${model} --release_version release_v6`

For multi-choice question answering tasks (Winogrande, ARC-C, ARC-E, BoolQ, HellaSwag, MMLU, OpenBookQA, RTE), 
we evaluate using lm_eval on a single GPU with a batch size equal 16.

Other parameters are set to default.

### Metrics

We report the metric from the first row printed by lm_eval.

For example, for IFEval, we report inst_level_loose_acc=0.8921 given the lm_eval's output:

|Tasks |Version|Filter|n-shot|        Metric         |   |Value |   |Stderr|
|------|------:|------|-----:|-----------------------|---|-----:|---|------|
|ifeval|      4|none  |     0|inst_level_loose_acc   |↑  |0.8921|±  |   N/A|
|      |       |none  |     0|inst_level_strict_acc  |↑  |0.8585|±  |   N/A|
|      |       |none  |     0|prompt_level_loose_acc |↑  |0.8373|±  |0.0159|
|      |       |none  |     0|prompt_level_strict_acc|↑  |0.7930|±  |0.0174|


### Results

| Model                            | Winogrande | ARC-C | ARC-E | BoolQ | HellaSwag | MMLU | OpenBookQA | RTE  | AVG  |
|----------------------------------|------------|-------|-------|-------|-----------|------|------------|------|------|
| Qwen3-30B-A3B-Instruct-2507      | 73.2       | 60.7  | 85.1  | 88.7  | 61.2      | 80.1 | 32.4       | 76.5 | 69.7 |
| Qwen3-30B-A3B-Instruct-2507-REAM | 71.8       | 51.9  | 79.1  | 88.5  | 57.6      | 70.1 | 30.0       | 77.6 | 65.8 |


| Model                            | IFeval | AIME25 | GSM8K | GPQA-D | HumanEval | LiveCodeBench | AVG   |
|----------------------------------|--------|--------|-------|--------|-----------|---------------|-------|
| Qwen3-30B-A3B-Instruct-2507      | 90.4   | 56.7   | 89.3  | 47.0   | 93.3      | 48.6          |  70.9 | 
| Qwen3-30B-A3B-Instruct-2507-REAM | 89.2   | 66.7   | 88.1  | 38.9   | 86.6      | 36.9          |  67.7 |

## License

Please refer to the license of the original model [Qwen/Qwen3-30B-A3B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507).