---
license: apache-2.0
language:
- zh
- en
pipeline_tag: text-generation
library_name: transformers
---
<div align="center">
<img src="https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm_logo.png?raw=true" width="500em" ></img> 
</div>

<p align="center">
<a href="https://github.com/OpenBMB/MiniCPM/" target="_blank">GitHub Repo</a> |
<a href="TODO_TECHNICAL_REPORT_LINK" target="_blank">Technical Report</a> 
</p>
<p align="center">
👋 Join us on <a href="https://discord.gg/3cGQn9b3YM" target="_blank">Discord</a> and <a href="https://github.com/OpenBMB/MiniCPM/blob/main/assets/wechat.jpg" target="_blank">WeChat</a>
</p>

## Introduction

BitCPM4-CANN-1B-unquantized is the **unquantized QAT training checkpoint** of the BitCPM4-CANN-1B model. This model stores the raw quantization-aware training (QAT) parameters **before** fake-quantizer fusion—the ternary fake quantizers are defined in `modeling.py` and applied during forward propagation.

> ⚠️ **This model is NOT intended for direct inference.** It is designed as the starting point for fine-tuning BitCPM4-CANN. If you need a model for inference, please use the pseudo-quantized version: [openbmb/BitCPM4-CANN-0.5B](https://huggingface.co/openbmb/BitCPM4-CANN-0.5B).

### Key Characteristics

- 🎯 **Purpose**: Fine-tuning only. The model weights are un-fused QAT parameters with fake quantizers embedded in the `modeling.py` forward logic.
- 🔬 **Ternary Fake Quantizer**: The forward pass in `modeling.py` contains ternary quantization logic (mapping weights to {-1, 0, 1} with group-wise scaling), which ensures the model continues learning under ternary constraints during fine-tuning.
- 🔄 **Post-Training Conversion**: After fine-tuning, the model can be converted to pseudo-quantized format using the provided `qat-convert.py` script.

## BitCPM4-CANN Model Family

| Model | HuggingFace (Inference) | HuggingFace (Fine-tuning) |
|-------|-------------------------|---------------------------|
| BitCPM4-CANN-0.5B | [openbmb/BitCPM4-CANN-0.5B](https://huggingface.co/openbmb/BitCPM4-CANN-0.5B) | [openbmb/BitCPM4-CANN-0.5B-unquantized](https://huggingface.co/openbmb/BitCPM4-CANN-0.5B-unquantized) |
| BitCPM4-CANN-1B | [openbmb/BitCPM4-CANN-1B](https://huggingface.co/openbmb/BitCPM4-CANN-1B) | [openbmb/BitCPM4-CANN-1B-unquantized](https://huggingface.co/openbmb/BitCPM4-CANN-1B-unquantized) |
| BitCPM4-CANN-3B | [openbmb/BitCPM4-CANN-3B](https://huggingface.co/openbmb/BitCPM4-CANN-3B) | [openbmb/BitCPM4-CANN-3B-unquantized](https://huggingface.co/openbmb/BitCPM4-CANN-3B-unquantized) |
| BitCPM4-CANN-8B | [openbmb/BitCPM4-CANN-8B](https://huggingface.co/openbmb/BitCPM4-CANN-8B) | [openbmb/BitCPM4-CANN-8B-unquantized](https://huggingface.co/openbmb/BitCPM4-CANN-8B-unquantized) |

## Usage

### Fine-tuning

This model is designed for fine-tuning with frameworks that support custom modeling code. The critical requirement is that **the forward pass must go through the `modeling.py` file bundled with this model**, which contains the ternary fake quantizer logic. This ensures the model parameters remain compatible with ternary quantization constraints throughout fine-tuning.

#### Supported Fine-tuning Frameworks

- **DeepSpeed** (recommended): See [MiniCPM Fine-tuning Guide](https://github.com/OpenBMB/MiniCPM/tree/main/finetune)
- **LLaMA Factory**: Supports custom model loading with `trust_remote_code=True`
- **Other Frameworks**: Any framework that supports HuggingFace-compatible model loading with custom modeling code

#### Important: Ensure Fake Quantizer is Active

When fine-tuning, you **must** ensure:
1. Load the model with `trust_remote_code=True` so that the custom `modeling.py` (containing the ternary quantizer) is used.
2. The forward pass during training goes through the ternary quantizer defined in `modeling.py`—do NOT replace or bypass the model's forward logic.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

path = 'openbmb/BitCPM4-CANN-1B-unquantized'
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    path,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True
)

# Proceed with your fine-tuning pipeline (DeepSpeed, LLaMA Factory, etc.)
# The ternary fake quantizer in modeling.py will be applied automatically during forward pass.
```

### Post-Fine-tuning Conversion

After fine-tuning is complete, use the `qat-convert.py` script to fuse the fake quantizer and produce the pseudo-quantized model weights that can be used for inference:

```bash
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 then be loaded for inference in the same way as [openbmb/BitCPM4-CANN-1B](https://huggingface.co/openbmb/BitCPM4-CANN-1B)—no special quantization libraries required.

## Workflow Summary

```
┌─────────────────────────────────┐
│  BitCPM4-CANN-1B-unquantized  │   ← This model (QAT parameters + fake quantizer in modeling.py)
└───────────────┬─────────────────┘
                │
                ▼  Fine-tune (DeepSpeed / LLaMA Factory / ...)
┌─────────────────────────────────┐
│   Fine-tuned pytorch.bin         │   ← Still contains un-fused QAT parameters
└───────────────┬─────────────────┘
                │
                ▼  python qat-convert.py --quant_type ternary --group_size -1
┌─────────────────────────────────┐
│  Pseudo-quantized pytorch.bin    │   ← Ready for inference (same format as BitCPM4-CANN-0.5B)
└─────────────────────────────────┘
```

## Technical Background

BitCPM4-CANN uses a ternary quantizer that maps each weight group to {-1, 0, 1} scaled by a group-wise factor, trained with Straight-Through Estimator (STE) for gradient flow. The unquantized checkpoint preserves the full-precision latent weights alongside the quantizer parameters, allowing the model to continue learning under quantization constraints during fine-tuning.

For full technical details, please refer to our [Technical Report](TODO_TECHNICAL_REPORT_LINK).

## Statement
- As a language model, BitCPM4-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 BitCPM4-CANN does not represent the viewpoints or positions of the model developers. 
- Therefore, when using content generated by BitCPM4-CANN, users should take full responsibility for evaluating and verifying it on their own.

## LICENSE
- This repository and BitCPM4-CANN models are released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License. 

## Citation
- Please cite our technical report if you find our work valuable.

```bibtex
@article{bitcpm4cann,
  title={{BitCPM-CANN}: Native 1.58-Bit Large Language Model Training on Ascend NPU},
  author={BitCPM Team},
  year={2026}
}
```