Instructions to use stabilityai/japanese-stablelm-base-alpha-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use stabilityai/japanese-stablelm-base-alpha-7b with Transformers:

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

pipe = pipeline("text-generation", model="stabilityai/japanese-stablelm-base-alpha-7b", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("stabilityai/japanese-stablelm-base-alpha-7b", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use stabilityai/japanese-stablelm-base-alpha-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "stabilityai/japanese-stablelm-base-alpha-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "stabilityai/japanese-stablelm-base-alpha-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/stabilityai/japanese-stablelm-base-alpha-7b

SGLang

How to use stabilityai/japanese-stablelm-base-alpha-7b 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 "stabilityai/japanese-stablelm-base-alpha-7b" \
    --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": "stabilityai/japanese-stablelm-base-alpha-7b",
		"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 "stabilityai/japanese-stablelm-base-alpha-7b" \
        --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": "stabilityai/japanese-stablelm-base-alpha-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use stabilityai/japanese-stablelm-base-alpha-7b with Docker Model Runner:
```
docker model run hf.co/stabilityai/japanese-stablelm-base-alpha-7b
```

Add fp16/int8 weights

by mkshing - opened Aug 10, 2023

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+276

-0

mkshing

Aug 10, 2023

•

edited Aug 10, 2023

This PR enables to use this model with Colab Free plan by int8 quantization.
Here's the link to the demo in colab.

https://colab.research.google.com/github/mkshing/notebooks/blob/main/stabilityai_japanese_stablelm_alpha_7b.ipynb

add int874641c11

add fp165a6026af

mkshing changed pull request status to open Aug 10, 2023

leemeng

Aug 10, 2023

Generally LGTM! by the way, if we don't include variant="int8" in the from_pretrained, it will just load the original fp32 version, is that correct?

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    variant="int8",
    low_cpu_mem_usage=True,
    load_in_8bit=True,
)

mkshing

Aug 10, 2023

•

edited Aug 10, 2023

Exactly!
So, if I'm correct, it loads fp32 weights first and convert to int8 in this case.


model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
-   variant="int8",
    low_cpu_mem_usage=True,
    load_in_8bit=True,
)

leemeng

Aug 10, 2023

nice! let's merge this. By the way, do you want to also include the variant as a colab dropdown (with default use int8) like model_id so people can be aware of that?

leemeng changed pull request status to merged Aug 10, 2023

mkshing

Aug 10, 2023

@leemeng sure! I will add some comments that only int8 works in colab free.

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