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", device_map="auto")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-1_6b")
model = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-2-1_6b", device_map="auto")

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

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	---
	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`](https://huggingface.co/stabilityai/stablelm-2-1_6b/tree/global_step420000/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](https://github.com/EleutherAI/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:

	```python
	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](https://huggingface.co/stabilityai/stablelm-2-1_6b/blob/main/LICENSE). If you'd like to use this model for commercial products or purposes, please contact us [here](https://stability.ai/membership) to learn more.

	## Acknowledgements

	- Dakota Mahan for creating the ZeRO optimizer state merging script.

	## Citation

	```bibtex
	@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}
	}
	```