0xSero / INTELLECT-3-REAP-50-W4A16

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Model's Last Updated: January 05 2026
text-generation

Introduction of INTELLECT-3-REAP-50-W4A16

Model Details of INTELLECT-3-REAP-50-W4A16

INTELLECT-3-REAP-50-W4A16

W4A16 GPTQ quantized version of INTELLECT-3-REAP-50 using Intel AutoRound.

Model Details
Property Value
Base Model 0xSero/INTELLECT-3-REAP-50
Original Model PrimeIntellect/INTELLECT-3 (248B MoE)
Architecture GLM-4 MoE (glm4_moe)
Expert Compression 50% (64 experts per layer)
Quantization W4A16 (4-bit weights, 16-bit activations)
Format GPTQ SafeTensors
Size 7.5 GB
Group Size 128
Quantization Configuration
method: AutoRound (Intel)
format: auto_gptq
bits: 4
group_size: 128
iters: 200
seqlen: 2048
dataset: NeelNanda/pile-10k
REAP Pruning Details

The base model was pruned using Cerebras REAP with:

  • Dataset : 0xSero/glm47-reap-calibration-v2 (1360 samples)
  • Distance : angular
  • Compression : 50% (64 of 128 experts removed per layer)
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "0xSero/INTELLECT-3-REAP-50-W4A16",
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("0xSero/INTELLECT-3-REAP-50-W4A16", 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 7.5GB
Acknowledgments

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

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