BitCPM4 : The era of 1 bit LLMs is here
BitCPM4 vs BitNet
A few months back, Microsoft released Bitnet 1.5b model and alongside the revolutionary paper “The Era of 1-Bit LLM”, where they introduced that LLMs can be compressed highly by storing weights as ternary weights in one bit.
https://medium.com/media/96cc422a7ff3d2ff6cc8902a279d5e9c/href
Now, a worthy opponent to BitNet1.5B is out and it’s BitCPM4, Another LLM which can store its weights/parameters as ternary weights in one bit. Though the way these models are designed is completely different, but they serve the same.
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What is BitCMP4?
BitCPM4 is one of the variants of mini CPM 4 model series which has been released quite recently, specialised for edge devices. Unlike Bitnet 1.5b which is trained from scratch on ternary weights, BitCPM4 is more of a quantized version of MiniCPM4.
https://medium.com/media/b019687782df02a841e56a0b047760b4/href
BitCPM4 doesn’t train from scratch. It pulls a fast one — start from a pretrained FP8 model, and gently coerce it into becoming ternary via a two-stage training recipe.
Here’s the gist:
- Stage 1: Fine-tune the FP8 checkpoint with a learning rate of 1e-2.
- Stage 2: Apply QAT (Quantization-Aware Training) at a reduced learning rate 5e-3, with a warmup for stability.
- Token Budget: Only about 40% of training tokens are spent on QAT.
No need for 4 trillion tokens. No need for thousands of A100s GPUs. Just efficient reuse.
Benchmarks
BitCPM4 may be modest in size (0.5B and 1B models), but it punches above its weight.
- BitCPM4–0.5B outperforms Qwen3–0.6B on standard benchmarks like MMLU and C-EVAL.
- BitCPM4–1B keeps pace with BitNet-2B, which was trained using 10x more tokens.
- Training cost? A mere 10% of what BitNet burned through.
This isn’t just good — it’s suspiciously efficient.
BitCPM4 vs BitNet 1.5B
BitCPM4 is looking better than BitNet 1.5B even though it’s a quantized version of MiniCPM4. Below is a detailed difference between the two models, and why you should opt for BitCPM4 asap.
Here’s the catch: BitNet is great if you’ve got enterprise-scale infrastructure and want every last drop of edge inference performance. BitCPM4 is for the rest of us — who want solid, deployable low-bit LLMs without frying the cloud budget.
What Makes BitCPM4 Work?
- It’s modular. Works with pretrained checkpoints, so you don’t need to reinvent the model.
- It’s scalable. Start small. Move to larger models as quantization techniques mature.
- It’s efficient. No 4T-token marathon runs — just a surgical strike with QAT.
- It’s flexible. Plays nice with existing toolchains, unlike BitNet which needs a custom runtime.
A few problems though
The smallest BitCPM4 models struggle with math and code-heavy benchmarks. Not shocking — ternary weights plus small model size is a rough combo for symbol-heavy reasoning. But the roadmap is clear: scale up, keep the QAT-efficient tricks, and you’ve got a lean, low-bit model that doesn’t compromise on brains.
Also, operator support for ultra-low-bit math is still lacking in most frameworks. Until those catch up, BitCPM4 can’t flex its full muscle.
Final Thought
If BitNet is the Ferrari of ternary models — fast, slick, custom-built — BitCPM4 is the Toyota that runs forever, sips fuel, and gets the job done without drama. In a world chasing 1-bit dreams, BitCPM4 is the practical low-bit path we didn’t know we needed.
The model is open-sourced and can be explored on HuggingFace
openbmb/BitCPM4-1B · Hugging Face
BitCPM4 : The era of 1 bit LLMs is here was originally published in Data Science in Your Pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.
