Instructions to use ibm-research/PowerLM-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ibm-research/PowerLM-3b with Transformers:

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

pipe = pipeline("text-generation", model="ibm-research/PowerLM-3b")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ibm-research/PowerLM-3b")
model = AutoModelForCausalLM.from_pretrained("ibm-research/PowerLM-3b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ibm-research/PowerLM-3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ibm-research/PowerLM-3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ibm-research/PowerLM-3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ibm-research/PowerLM-3b

SGLang

How to use ibm-research/PowerLM-3b 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 "ibm-research/PowerLM-3b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ibm-research/PowerLM-3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "ibm-research/PowerLM-3b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ibm-research/PowerLM-3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ibm-research/PowerLM-3b with Docker Model Runner:
```
docker model run hf.co/ibm-research/PowerLM-3b
```

mayank-mishra commited on Aug 27, 2024

Commit

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@@ -136,12 +136,9 @@ tokenizer = AutoTokenizer.from_pretrained(model_path)
 model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
 model.eval()
 # change input text as desired
-chat = [
-    { "role": "user", "content": "Write a code to find the maximum value in a list of numbers." },
-]
-chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
 # tokenize the text
-input_tokens = tokenizer(chat, return_tensors="pt")
 # transfer tokenized inputs to the device
 for i in input_tokens:
     input_tokens[i] = input_tokens[i].to(device)

 model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
 model.eval()
 # change input text as desired
+prompt = "Write a code to find the maximum value in a list of numbers."
 # tokenize the text
+input_tokens = tokenizer(prompt, return_tensors="pt")
 # transfer tokenized inputs to the device
 for i in input_tokens:
     input_tokens[i] = input_tokens[i].to(device)