Instructions to use openbmb/BitCPM4-CANN-1B-unquantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openbmb/BitCPM4-CANN-1B-unquantized with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="openbmb/BitCPM4-CANN-1B-unquantized", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("openbmb/BitCPM4-CANN-1B-unquantized", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("openbmb/BitCPM4-CANN-1B-unquantized", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use openbmb/BitCPM4-CANN-1B-unquantized with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openbmb/BitCPM4-CANN-1B-unquantized"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openbmb/BitCPM4-CANN-1B-unquantized",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/openbmb/BitCPM4-CANN-1B-unquantized

SGLang

How to use openbmb/BitCPM4-CANN-1B-unquantized with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "openbmb/BitCPM4-CANN-1B-unquantized" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openbmb/BitCPM4-CANN-1B-unquantized",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "openbmb/BitCPM4-CANN-1B-unquantized" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openbmb/BitCPM4-CANN-1B-unquantized",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use openbmb/BitCPM4-CANN-1B-unquantized with Docker Model Runner:
```
docker model run hf.co/openbmb/BitCPM4-CANN-1B-unquantized
```

BitCPM4-CANN-1B-unquantized / README.md

guanwenyu1995

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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-1B-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-1B](https://huggingface.co/openbmb/BitCPM4-CANN-1B)—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}
	}
	```