Instructions to use openbmb/BitCPM-CANN-0.5B-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openbmb/BitCPM-CANN-0.5B-gguf with Transformers:

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

pipe = pipeline("text-generation", model="openbmb/BitCPM-CANN-0.5B-gguf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("openbmb/BitCPM-CANN-0.5B-gguf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use openbmb/BitCPM-CANN-0.5B-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf openbmb/BitCPM-CANN-0.5B-gguf:BF16
# Run inference directly in the terminal:
llama cli -hf openbmb/BitCPM-CANN-0.5B-gguf:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf openbmb/BitCPM-CANN-0.5B-gguf:BF16
# Run inference directly in the terminal:
llama cli -hf openbmb/BitCPM-CANN-0.5B-gguf:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf openbmb/BitCPM-CANN-0.5B-gguf:BF16
# Run inference directly in the terminal:
./llama-cli -hf openbmb/BitCPM-CANN-0.5B-gguf:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf openbmb/BitCPM-CANN-0.5B-gguf:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf openbmb/BitCPM-CANN-0.5B-gguf:BF16

Use Docker

docker model run hf.co/openbmb/BitCPM-CANN-0.5B-gguf:BF16

LM Studio
Jan

vLLM

How to use openbmb/BitCPM-CANN-0.5B-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openbmb/BitCPM-CANN-0.5B-gguf"
# 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/BitCPM-CANN-0.5B-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/openbmb/BitCPM-CANN-0.5B-gguf:BF16

SGLang

How to use openbmb/BitCPM-CANN-0.5B-gguf 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/BitCPM-CANN-0.5B-gguf" \
    --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/BitCPM-CANN-0.5B-gguf",
		"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/BitCPM-CANN-0.5B-gguf" \
        --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/BitCPM-CANN-0.5B-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use openbmb/BitCPM-CANN-0.5B-gguf with Ollama:
```
ollama run hf.co/openbmb/BitCPM-CANN-0.5B-gguf:BF16
```

Unsloth Studio

How to use openbmb/BitCPM-CANN-0.5B-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for openbmb/BitCPM-CANN-0.5B-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for openbmb/BitCPM-CANN-0.5B-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for openbmb/BitCPM-CANN-0.5B-gguf to start chatting

Docker Model Runner
How to use openbmb/BitCPM-CANN-0.5B-gguf with Docker Model Runner:
```
docker model run hf.co/openbmb/BitCPM-CANN-0.5B-gguf:BF16
```

Lemonade

How to use openbmb/BitCPM-CANN-0.5B-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull openbmb/BitCPM-CANN-0.5B-gguf:BF16

Run and chat with the model

lemonade run user.BitCPM-CANN-0.5B-gguf-BF16

List all available models

lemonade list

Atomic Chat

guanwenyu1995 commited on May 23

Commit

9fca09b

verified ·

1 Parent(s): 427f2cf

Update README naming from BitCPM4 to BitCPM

Browse files

Files changed (1) hide show

README.md +16 -16

README.md CHANGED Viewed

@@ -18,14 +18,14 @@ library_name: transformers
 👋 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>
-> **This repository hosts the GGUF (llama.cpp) quantized version of [BitCPM4-CANN-0.5B](https://huggingface.co/openbmb/BitCPM4-CANN-0.5B/).** For the pseudo-quantized weights and the full model card, please refer to [BitCPM4-CANN-0.5B](https://huggingface.co/openbmb/BitCPM4-CANN-0.5B/).
 ## 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.
-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).
 ### Key Features
@@ -38,27 +38,27 @@ 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
 | 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) |
 ## Usage
 ### Inference with Transformers
-Since BitCPM4-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-0.5B'
 device = "cuda"
 tokenizer = AutoTokenizer.from_pretrained(path)
 model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map=device, trust_remote_code=True)
@@ -96,7 +96,7 @@ 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.
 | Task | 8B FP | 8B Ternary | 3B FP | 3B Ternary | 1B FP | 1B Ternary | 0.5B FP | 0.5B Ternary |
@@ -145,19 +145,19 @@ 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.
 - 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}

 👋 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>
+> **This repository hosts the GGUF (llama.cpp) quantized version of [BitCPM-CANN-0.5B](https://huggingface.co/openbmb/BitCPM-CANN-0.5B/).** For the pseudo-quantized weights and the full model card, please refer to [BitCPM-CANN-0.5B](https://huggingface.co/openbmb/BitCPM-CANN-0.5B/).
 ## Introduction
+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—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
 > 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.
+## BitCPM-CANN Model Family
 | Model | HuggingFace | 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 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/BitCPM-CANN-0.5B'
 device = "cuda"
 tokenizer = AutoTokenizer.from_pretrained(path)
 model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map=device, trust_remote_code=True)
 ### Main Results
+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 |
 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, 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}