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

Libraries

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

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

pipe = pipeline("text-generation", model="openbmb/BitCPM-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/BitCPM-CANN-1B-unquantized", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("openbmb/BitCPM-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 Settings

vLLM

How to use openbmb/BitCPM-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/BitCPM-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/BitCPM-CANN-1B-unquantized",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use openbmb/BitCPM-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/BitCPM-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/BitCPM-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/BitCPM-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/BitCPM-CANN-1B-unquantized",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

guanwenyu1995 commited on 15 days ago

Commit

60238a0

verified ·

1 Parent(s): f7090a8

Upload config.json with huggingface_hub

Browse files

Files changed (1) hide show

config.json +5 -5

config.json CHANGED Viewed

@@ -12,14 +12,14 @@
     "eos_token_id": [2,73440],
     "pad_token_id": 2,
     "hidden_act": "silu",
-    "hidden_size": 2560,
     "initializer_range": 0.1,
-    "intermediate_size": 10240,
     "head_dim": 128,
     "max_position_embeddings": 32768,
     "model_type": "llama",
-    "num_attention_heads": 32,
-    "num_hidden_layers": 32,
     "num_key_value_heads": 2,
     "rms_norm_eps": 1e-06,
     "rope_scaling": {
@@ -33,4 +33,4 @@
     "transformers_version": "4.36.0",
     "use_cache": true,
     "vocab_size": 73448
-}

     "eos_token_id": [2,73440],
     "pad_token_id": 2,
     "hidden_act": "silu",
+    "hidden_size": 2048,
     "initializer_range": 0.1,
+    "intermediate_size": 6144,
     "head_dim": 128,
     "max_position_embeddings": 32768,
     "model_type": "llama",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 28,
     "num_key_value_heads": 2,
     "rms_norm_eps": 1e-06,
     "rope_scaling": {
     "transformers_version": "4.36.0",
     "use_cache": true,
     "vocab_size": 73448
+}