Update README naming from BitCPM4 to BitCPM

README.md

@@ -20,9 +20,9 @@ library_name: transformers
 
 ## Introduction
 
-BitCPM4-CANN is the first end-to-end 1.58-bit (ternary) large language model training system natively built on Huawei Ascend NPU. The system integrates quantization-aware training (QAT) into the Megatron-LM framework with MindSpeed acceleration, covering the full training stack from custom ternary operators to distributed parallel training on Ascend 910B.
+BitCPM-CANN is the first end-to-end 1.58-bit (ternary) large language model training system natively built on Huawei Ascend NPU. The system integrates quantization-aware training (QAT) into the Megatron-LM framework with MindSpeed acceleration, covering the full training stack from custom ternary operators to distributed parallel training on Ascend 910B.
 
-We train a family of four models—BitCPM4-CANN-0.5B/1B/3B/8B—and evaluate them against their full-precision MiniCPM4 counterparts across 11 benchmarks. The 1B/3B/8B models retain **95.7%–97.2%** of full-precision performance, while enabling approximately **6× memory reduction** at inference time. QAT introduces only **5% training throughput overhead** (148 vs. 155 TFLOP/s per NPU).
+We train a family of four models—BitCPM-CANN-0.5B/1B/3B/8B—and evaluate them against their full-precision MiniCPM4 counterparts across 11 benchmarks. The 1B/3B/8B models retain **95.7%–97.2%** of full-precision performance, while enabling approximately **6× memory reduction** at inference time. QAT introduces only **5% training throughput overhead** (148 vs. 155 TFLOP/s per NPU).
 
 ### Key Features
 
@@ -35,27 +35,27 @@ We train a family of four models—BitCPM4-CANN-0.5B/1B/3B/8B—and evaluate the
 
 > The models in this repository are in **pseudo-quantized (fake quantization) format**. This means the weights are stored in standard floating-point format with ternary values already applied during training. You can load and run inference with these models **exactly the same way as full-precision models**—no special quantization libraries or custom kernels are required.
 
-## BitCPM4-CANN Model Family
+## BitCPM-CANN Model Family
 
 | Model | HuggingFace | GGUF |
 |-------|-------------|------|
-| BitCPM4-CANN-0.5B | [openbmb/BitCPM4-CANN-0.5B](https://huggingface.co/openbmb/BitCPM4-CANN-0.5B) | [openbmb/BitCPM4-CANN-0.5B-gguf](https://huggingface.co/openbmb/BitCPM4-CANN-0.5B-gguf) |
-| BitCPM4-CANN-1B | [openbmb/BitCPM4-CANN-1B](https://huggingface.co/openbmb/BitCPM4-CANN-1B) | [openbmb/BitCPM4-CANN-1B-gguf](https://huggingface.co/openbmb/BitCPM4-CANN-1B-gguf) |
-| BitCPM4-CANN-3B | [openbmb/BitCPM4-CANN-3B](https://huggingface.co/openbmb/BitCPM4-CANN-3B) | [openbmb/BitCPM4-CANN-3B-gguf](https://huggingface.co/openbmb/BitCPM4-CANN-3B-gguf) |
-| BitCPM4-CANN-8B | [openbmb/BitCPM4-CANN-8B](https://huggingface.co/openbmb/BitCPM4-CANN-8B) | [openbmb/BitCPM4-CANN-8B-gguf](https://huggingface.co/openbmb/BitCPM4-CANN-8B-gguf) |
+| BitCPM-CANN-0.5B | [openbmb/BitCPM-CANN-0.5B](https://huggingface.co/openbmb/BitCPM-CANN-0.5B) | [openbmb/BitCPM-CANN-0.5B-gguf](https://huggingface.co/openbmb/BitCPM-CANN-0.5B-gguf) |
+| BitCPM-CANN-1B | [openbmb/BitCPM-CANN-1B](https://huggingface.co/openbmb/BitCPM-CANN-1B) | [openbmb/BitCPM-CANN-1B-gguf](https://huggingface.co/openbmb/BitCPM-CANN-1B-gguf) |
+| BitCPM-CANN-3B | [openbmb/BitCPM-CANN-3B](https://huggingface.co/openbmb/BitCPM-CANN-3B) | [openbmb/BitCPM-CANN-3B-gguf](https://huggingface.co/openbmb/BitCPM-CANN-3B-gguf) |
+| BitCPM-CANN-8B | [openbmb/BitCPM-CANN-8B](https://huggingface.co/openbmb/BitCPM-CANN-8B) | [openbmb/BitCPM-CANN-8B-gguf](https://huggingface.co/openbmb/BitCPM-CANN-8B-gguf) |
 
 ## Usage
 
 ### Inference with Transformers
 
-Since BitCPM4-CANN models are in pseudo-quantized format, you can use them exactly like standard full-precision models:
+Since BitCPM-CANN models are in pseudo-quantized format, you can use them exactly like standard full-precision models:
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 torch.manual_seed(0)
 
-path = 'openbmb/BitCPM4-CANN-1B'
+path = 'openbmb/BitCPM-CANN-1B'
 device = "cuda"
 tokenizer = AutoTokenizer.from_pretrained(path)
 model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map=device, trust_remote_code=True)
@@ -93,7 +93,7 @@ print(responds)
 
 ### Main Results
 
-BitCPM4-CANN models are evaluated against their full-precision MiniCPM4 counterparts across 11 benchmarks spanning commonsense reasoning, domain knowledge, and mathematics & reasoning.
+BitCPM-CANN models are evaluated against their full-precision MiniCPM4 counterparts across 11 benchmarks spanning commonsense reasoning, domain knowledge, and mathematics & reasoning.
 
 | Task | 8B FP | 8B Ternary | 3B FP | 3B Ternary | 1B FP | 1B Ternary | 0.5B FP | 0.5B Ternary |
 |------|-------|------------|-------|------------|-------|------------|---------|--------------|
@@ -141,19 +141,19 @@ The system is built as a four-layer vertical stack on Ascend NPU:
 For full technical details, please refer to our [Technical Report](https://github.com/OpenBMB/MiniCPM/blob/main/docs/BitCPM_CANN.pdf).
 
 ## Statement
-- As a language model, BitCPM4-CANN generates content by learning from a vast amount of text. 
+- 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 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.
+- 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 BitCPM4-CANN models are released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) 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{bitcpm4cann,
+@article{bitcpmcann,
   title={{BitCPM-CANN}: Native 1.58-Bit Large Language Model Training on Ascend NPU},
   author={BitCPM Team},
   year={2026}