BitCPM-CANN-0.5B-unquantized

Overview

BitCPM-CANN-0.5B-unquantized is the unquantized QAT (Quantization-Aware Training) checkpoint of BitCPM-CANN-0.5B, designed for continued pre-training and fine-tuning. It preserves full-precision latent weights with ternary fake quantizers (weights → {-1, 0, 1} with group-wise scaling, trained via STE) defined in modeling.py, enabling the model to keep learning under quantization constraints. For technical details, see our Technical Report.

⚠️ This model is NOT for direct inference. For inference, use the pseudo-quantized version: openbmb/BitCPM-CANN-0.5B.

Continued Pre-training & Fine-tuning

The only requirement is that the forward pass must go through the bundled modeling.py (which contains the ternary fake quantizer). Load with trust_remote_code=True and do NOT replace or bypass the model's forward logic.

Option 1: DeepSpeed (Recommended)

We provide ready-to-use training scripts in the example directory (using the 1B model as an example):

• Continued pre-training: example/run.sh + example/train.py
• SFT (Supervised Fine-tuning): example/run_sft.sh + example/train_sft.py

Quick start:

# Continued pre-training
cd example && bash run.sh

# Supervised fine-tuning
cd example && bash run_sft.sh

Option 2: HuggingFace-compatible Frameworks

Any framework that supports HuggingFace model loading with custom code can be used, such as LLaMA Factory, HuggingFace Trainer, etc. The key is to ensure trust_remote_code=True:

from transformers import AutoModelForCausalLM, AutoTokenizer

path = 'openbmb/BitCPM-CANN-0.5B-unquantized'
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    path,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True
)

# Use with your preferred framework (LLaMA Factory, HF Trainer, etc.)
# The ternary fake quantizer in modeling.py is applied automatically during forward pass.

Post-Training Conversion

After training, use qat-convert.py to fuse the fake quantizer and produce inference-ready pseudo-quantized weights:

python qat-convert.py \
    --input_bin <path-to-finetuned-pytorch.bin> \
    --output <path-to-output-pseudo-quantized-pytorch.bin> \
    --quant_type ternary \
    --group_size -1

The converted model can be loaded for inference in the same way as openbmb/BitCPM-CANN-0.5B—no special quantization libraries required.

Workflow

┌─────────────────────────────────┐
│  BitCPM-CANN-0.5B-unquantized  │   ← This model (QAT checkpoint + fake quantizer in modeling.py)
└───────────────┬─────────────────┘
                │
                ▼  Train (DeepSpeed / LLaMA Factory / HF Trainer / ...)
┌─────────────────────────────────┐
│   Fine-tuned checkpoint          │   ← Still contains un-fused QAT parameters
└───────────────┬─────────────────┘
                │
                ▼  python qat-convert.py --quant_type ternary --group_size -1
┌─────────────────────────────────┐
│  Pseudo-quantized model          │   ← Ready for inference (same format as BitCPM-CANN-0.5B)
└─────────────────────────────────┘

BitCPM-CANN Model Family

Statement

• As a language model, BitCPM-CANN generates content by learning from a vast amount of text.
• However, it does not possess the ability to comprehend or express personal opinions or value judgments.
• Any content generated by BitCPM-CANN does not represent the viewpoints or positions of the model developers.
• Therefore, when using content generated by BitCPM-CANN, users should take full responsibility for evaluating and verifying it on their own.

LICENSE

• This repository and BitCPM-CANN models are released under the Apache-2.0 License.

Citation

• Please cite our technical report if you find our work valuable.

@article{bitcpmcann,
  title={{BitCPM-CANN}: Native 1.58-Bit Large Language Model Training on Ascend NPU},
  author={BitCPM Team},
  year={2026}
}