Create README.md

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="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-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
+)
+
+# 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-0.5B](https://huggingface.co/openbmb/BitCPM4-CANN-0.5B)—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}
+}
+```