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Model: openbmb/BitCPM-CANN-8B-unquantized
Source: Original Platform

README.md

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+---
+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-8B-unquantized is the **unquantized QAT (Quantization-Aware Training) checkpoint** of BitCPM-CANN-8B, 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-8B](https://huggingface.co/openbmb/BitCPM-CANN-8B).
+
+## 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-8B-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-8B-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-8B](https://huggingface.co/openbmb/BitCPM-CANN-8B)—no special quantization libraries required.
+
+## Workflow
+
+```
+┌─────────────────────────────────┐
+│  BitCPM-CANN-8B-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-8B)
+└─────────────────────────────────┘
+```
+
+## 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}
+}
+```