Instructions to use stabilityai/stablelm-2-1_6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use stabilityai/stablelm-2-1_6b with Transformers:

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

pipe = pipeline("text-generation", model="stabilityai/stablelm-2-1_6b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-1_6b")
model = AutoModelForMultimodalLM.from_pretrained("stabilityai/stablelm-2-1_6b")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use stabilityai/stablelm-2-1_6b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "stabilityai/stablelm-2-1_6b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "stabilityai/stablelm-2-1_6b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/stabilityai/stablelm-2-1_6b

SGLang

How to use stabilityai/stablelm-2-1_6b 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/stablelm-2-1_6b" \
    --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/stablelm-2-1_6b",
		"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/stablelm-2-1_6b" \
        --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/stablelm-2-1_6b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use stabilityai/stablelm-2-1_6b with Docker Model Runner:
```
docker model run hf.co/stabilityai/stablelm-2-1_6b
```

stablelm-2-1_6b / README.md

jon-tow

Update README.md

f12831a verified over 2 years ago

2.17 kB

license: other
language:
  - en
tags:
  - causal-lm

`Stable LM 2 1.6B` (global_step420000)

Description

Stable LM 2 1.6B is a 1.6 billion parameter decoder-only language model pre-trained on 2 trillion tokens of diverse multilingual and code datasets for two epochs.

Usage

This branch contains the training checkpoint for Stable LM 2 1.6B at step 420,000. It is the final checkpoint taken before cooldown. We provide the following contents in the global_step420000 directory:

bf16_zero_pp_mp_rank_00_optim_states.pt: The Adam states and FP32 weights for each parameter. You will need to port this to your optimizer format when importing into your training process.
mp_rank_00_model_states.pt: The model weights following the GPT-NeoX convention.
config.yml: The pre-training configuration file for this checkpoint. Linear learning rate cooldown should be taken from lr=0.0002529 to lr=0.0.

The model weights are also converted to HuggingFace transformers format and can be loaded with the following code:

from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-1_6b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-1_6b",
  trust_remote_code=True,
  torch_dtype="auto",
  revision="global_step420000"
)
model.cuda()

License

License: Stability AI Non-Commercial Research Community License. If you'd like to use this model for commercial products or purposes, please contact us here to learn more.

Acknowledgements

Dakota Mahan for creating the ZeRO optimizer state merging script.

Citation

@misc{StableLM-2-1.6B,
      url={[https://huggingface.co/stabilityai/stablelm-2-1_6b](https://huggingface.co/stabilityai/stablelm-2-1_6b)},
      title={Stable LM 2 1.6B},
      author={Stability AI Language Team}
}