jtl11/INTELLECT-3-REAP-50-heretic

This is a decensored version of 0xSero/INTELLECT-3-REAP-50, made using Heretic v1.1.0

Abliteration parameters

Parameter	Value
direction_index	26.50
attn.o_proj.max_weight	1.48
attn.o_proj.max_weight_position	27.79
attn.o_proj.min_weight	1.10
attn.o_proj.min_weight_distance	15.18
mlp.down_proj.max_weight	1.47
mlp.down_proj.max_weight_position	39.36
mlp.down_proj.min_weight	0.24
mlp.down_proj.min_weight_distance	17.75

Performance

Metric	This model	Original model (0xSero/INTELLECT-3-REAP-50)
KL divergence	0.0413	0 (by definition)
Refusals	3/100	60/100

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INTELLECT-3-REAP-50

50% expert-pruned version of PrimeIntellect/INTELLECT-3 using Cerebras REAP (Router-weighted Expert Activation Pruning).

Model Details

Property	Value
Base Model	PrimeIntellect/INTELLECT-3 (248B MoE)
Architecture	GLM-4 MoE (glm4_moe)
Compression	50% (64 experts pruned)
Remaining Experts	64 per layer
Parameters	~124B
Format	BF16 SafeTensors
Size	107 GB

REAP Configuration

dataset: 0xSero/glm47-reap-calibration-v2
samples: 1360
  - evol-codealpaca-v1: 700 (code generation)
  - xlam-function-calling-60k: 330 (function calling)
  - SWE-smith-trajectories: 330 (agentic multi-turn)
distance_measure: angular
seed: 42
model_max_length: 2048
compression_ratio: 0.50
prune_method: reap

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "0xSero/INTELLECT-3-REAP-50",
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("0xSero/INTELLECT-3-REAP-50", trust_remote_code=True)

messages = [{"role": "user", "content": "Write a Python function to calculate fibonacci numbers"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
outputs = model.generate(inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Related Models

Model	Compression	Format	Size
INTELLECT-3-REAP-50	50%	BF16	107GB
INTELLECT-3-REAP-50-W4A16	50%	W4A16 GPTQ	~30GB (coming soon)

Citation

@article{cerebras2025reap,
  title={REAP: Router-weighted Expert Activation Pruning for MoE Models},
  author={Cerebras Systems},
  year={2025}
}

Acknowledgments

• Prime Intellect - For sponsoring compute and creating INTELLECT-3
• Cerebras - For the REAP pruning methodology
• Pruned using the Cerebras REAP implementation

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This model was created as part of efficiency research for large MoE models.