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

Libraries

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

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

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

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("openbmb/BitCPM-CANN-0.5B-unquantized", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

docker model run hf.co/openbmb/BitCPM-CANN-0.5B-unquantized

SGLang

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

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

guanwenyu1995 commited on 1 day ago

Commit

1f9e819

verified ·

1 Parent(s): 8fd38e0

Update example naming from BitCPM4 to BitCPM

Browse files

Files changed (1) hide show

example/README.md +5 -5

example/README.md CHANGED Viewed

@@ -1,6 +1,6 @@
-# BitCPM4 Training Example
-This project provides scripts for continue pretraining (CPT) and supervised fine-tuning (SFT) of **BitCPM4-CANN-1B-unquantized**.
 ## File Description
@@ -49,7 +49,7 @@ The test dataset used is [C4-Pro](https://huggingface.co/datasets/gair-prox/c4-p
 Modify the path configuration in `run.sh`:
 ```bash
-MODEL_PATH="/path/to/BitCPM4-CANN-1B-unquantized/"
 DATA_PATH="/path/to/c4-pro/data/your_file.parquet"
 ```
@@ -70,7 +70,7 @@ The test dataset used is [UltraChat 200k](https://huggingface.co/datasets/Huggin
 Modify the path configuration in `run_sft.sh`:
 ```bash
-MODEL_PATH="/path/to/BitCPM4-CANN-1B-unquantized/"
 DATA_PATH="/path/to/ultrachat_200k/data/your_file.parquet"
 ```
@@ -102,4 +102,4 @@ Training log CSV files (corresponding to the loss curves above):
 ---
-These scripts provide a convenient, ready-to-use toolkit for QAT-aware continued pre-training and fine-tuning of BitCPM4-CANN models, so you can quickly adapt the model to your own data and tasks while preserving ternary quantization constraints.

+# BitCPM Training Example
+This project provides scripts for continue pretraining (CPT) and supervised fine-tuning (SFT) of **BitCPM-CANN-1B-unquantized**.
 ## File Description
 Modify the path configuration in `run.sh`:
 ```bash
+MODEL_PATH="/path/to/BitCPM-CANN-1B-unquantized/"
 DATA_PATH="/path/to/c4-pro/data/your_file.parquet"
 ```
 Modify the path configuration in `run_sft.sh`:
 ```bash
+MODEL_PATH="/path/to/BitCPM-CANN-1B-unquantized/"
 DATA_PATH="/path/to/ultrachat_200k/data/your_file.parquet"
 ```
 ---
+These scripts provide a convenient, ready-to-use toolkit for QAT-aware continued pre-training and fine-tuning of BitCPM-CANN models, so you can quickly adapt the model to your own data and tasks while preserving ternary quantization constraints.