README.md

---
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="https://github.com/OpenBMB/MiniCPM/blob/main/docs/BitCPM_CANN.pdf" 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>

## Overview

BitCPM-CANN-1B-unquantized is the **unquantized QAT (Quantization-Aware Training) checkpoint** of BitCPM-CANN-1B, 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](https://github.com/OpenBMB/MiniCPM/blob/main/docs/BitCPM_CANN.pdf).

> ⚠️ **This model is NOT for direct inference.** For inference, use the pseudo-quantized version: [openbmb/BitCPM-CANN-1B](https://huggingface.co/openbmb/BitCPM-CANN-1B).

## 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](https://huggingface.co/openbmb/BitCPM-CANN-1B-unquantized/tree/main/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:

```bash
# 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`:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

path = 'openbmb/BitCPM-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
)

# 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:

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

## Workflow

```
┌─────────────────────────────────┐
│  BitCPM-CANN-1B-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-1B)
└─────────────────────────────────┘
```

## BitCPM-CANN Model Family

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

## 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](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License. 

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

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