127 lines
6.2 KiB
Markdown
127 lines
6.2 KiB
Markdown
---
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license: apache-2.0
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language:
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- zh
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- en
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pipeline_tag: text-generation
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library_name: transformers
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---
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<div align="center">
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<img src="https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm_logo.png?raw=true" width="500em" ></img>
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</div>
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<p align="center">
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<a href="https://github.com/OpenBMB/MiniCPM/" target="_blank">GitHub Repo</a> |
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<a href="https://github.com/OpenBMB/MiniCPM/blob/main/docs/BitCPM_CANN.pdf" target="_blank">Technical Report</a>
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</p>
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<p align="center">
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👋 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>
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</p>
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## Overview
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BitCPM-CANN-3B-unquantized is the **unquantized QAT (Quantization-Aware Training) checkpoint** of BitCPM-CANN-3B, 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).
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> ⚠️ **This model is NOT for direct inference.** For inference, use the pseudo-quantized version: [openbmb/BitCPM-CANN-3B](https://huggingface.co/openbmb/BitCPM-CANN-3B).
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## Continued Pre-training & Fine-tuning
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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.
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### Option 1: DeepSpeed (Recommended)
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We provide ready-to-use training scripts in the [example](https://huggingface.co/openbmb/BitCPM-CANN-3B-unquantized/tree/main/example) directory (using the 1B model as an example):
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- **Continued pre-training**: `example/run.sh` + `example/train.py`
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- **SFT (Supervised Fine-tuning)**: `example/run_sft.sh` + `example/train_sft.py`
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Quick start:
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```bash
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# Continued pre-training
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cd example && bash run.sh
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# Supervised fine-tuning
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cd example && bash run_sft.sh
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```
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### Option 2: HuggingFace-compatible Frameworks
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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`:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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path = 'openbmb/BitCPM-CANN-3B-unquantized'
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tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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path,
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torch_dtype=torch.bfloat16,
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trust_remote_code=True
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)
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# Use with your preferred framework (LLaMA Factory, HF Trainer, etc.)
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# The ternary fake quantizer in modeling.py is applied automatically during forward pass.
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```
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## Post-Training Conversion
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After training, use `qat-convert.py` to fuse the fake quantizer and produce inference-ready pseudo-quantized weights:
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```bash
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python qat-convert.py \
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--input_bin <path-to-finetuned-pytorch.bin> \
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--output <path-to-output-pseudo-quantized-pytorch.bin> \
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--quant_type ternary \
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--group_size -1
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```
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The converted model can be loaded for inference in the same way as [openbmb/BitCPM-CANN-3B](https://huggingface.co/openbmb/BitCPM-CANN-3B)—no special quantization libraries required.
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## Workflow
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```
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┌─────────────────────────────────┐
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│ BitCPM-CANN-3B-unquantized │ ← This model (QAT checkpoint + fake quantizer in modeling.py)
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└───────────────┬─────────────────┘
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│
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▼ Train (DeepSpeed / LLaMA Factory / HF Trainer / ...)
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┌─────────────────────────────────┐
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│ Fine-tuned checkpoint │ ← Still contains un-fused QAT parameters
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└───────────────┬─────────────────┘
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│
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▼ python qat-convert.py --quant_type ternary --group_size -1
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┌─────────────────────────────────┐
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│ Pseudo-quantized model │ ← Ready for inference (same format as BitCPM-CANN-3B)
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└─────────────────────────────────┘
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```
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## BitCPM-CANN Model Family
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| Model | HuggingFace (Inference) | HuggingFace (Fine-tuning) |
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|-------|-------------------------|---------------------------|
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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## Statement
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- As a language model, BitCPM-CANN generates content by learning from a vast amount of text.
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- However, it does not possess the ability to comprehend or express personal opinions or value judgments.
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- Any content generated by BitCPM-CANN does not represent the viewpoints or positions of the model developers.
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- Therefore, when using content generated by BitCPM-CANN, users should take full responsibility for evaluating and verifying it on their own.
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## LICENSE
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- This repository and BitCPM-CANN models are released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License.
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## Citation
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- Please cite our technical report if you find our work valuable.
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```bibtex
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@article{bitcpmcann,
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title={{BitCPM-CANN}: Native 1.58-Bit Large Language Model Training on Ascend NPU},
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author={BitCPM Team},
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year={2026}
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}
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```
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