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

Libraries

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

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

pipe = pipeline("text-generation", model="stabilityai/stablelm-2-zephyr-1_6b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-zephyr-1_6b")
model = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-2-zephyr-1_6b")
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]:]))

llama-cpp-python

How to use stabilityai/stablelm-2-zephyr-1_6b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="stabilityai/stablelm-2-zephyr-1_6b",
	filename="stablelm-2-zephyr-1_6b-Q4_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use stabilityai/stablelm-2-zephyr-1_6b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf stabilityai/stablelm-2-zephyr-1_6b:Q4_0
# Run inference directly in the terminal:
llama-cli -hf stabilityai/stablelm-2-zephyr-1_6b:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf stabilityai/stablelm-2-zephyr-1_6b:Q4_0
# Run inference directly in the terminal:
llama-cli -hf stabilityai/stablelm-2-zephyr-1_6b:Q4_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf stabilityai/stablelm-2-zephyr-1_6b:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf stabilityai/stablelm-2-zephyr-1_6b:Q4_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf stabilityai/stablelm-2-zephyr-1_6b:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf stabilityai/stablelm-2-zephyr-1_6b:Q4_0

Use Docker

docker model run hf.co/stabilityai/stablelm-2-zephyr-1_6b:Q4_0

LM Studio
Jan

vLLM

How to use stabilityai/stablelm-2-zephyr-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-zephyr-1_6b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "stabilityai/stablelm-2-zephyr-1_6b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/stabilityai/stablelm-2-zephyr-1_6b:Q4_0

SGLang

How to use stabilityai/stablelm-2-zephyr-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-zephyr-1_6b" \
    --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": "stabilityai/stablelm-2-zephyr-1_6b",
		"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 "stabilityai/stablelm-2-zephyr-1_6b" \
        --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": "stabilityai/stablelm-2-zephyr-1_6b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use stabilityai/stablelm-2-zephyr-1_6b with Ollama:
```
ollama run hf.co/stabilityai/stablelm-2-zephyr-1_6b:Q4_0
```

Unsloth Studio new

How to use stabilityai/stablelm-2-zephyr-1_6b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for stabilityai/stablelm-2-zephyr-1_6b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for stabilityai/stablelm-2-zephyr-1_6b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for stabilityai/stablelm-2-zephyr-1_6b to start chatting

Docker Model Runner
How to use stabilityai/stablelm-2-zephyr-1_6b with Docker Model Runner:
```
docker model run hf.co/stabilityai/stablelm-2-zephyr-1_6b:Q4_0
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull stabilityai/stablelm-2-zephyr-1_6b:Q4_0

Run and chat with the model

lemonade run user.stablelm-2-zephyr-1_6b-Q4_0

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

`StableLM 2 Zephyr 1.6B`

Model Description

Stable LM 2 Zephyr 1.6B is a 1.6 billion parameter instruction tuned language model inspired by HugginFaceH4's Zephyr 7B training pipeline. The model is trained on a mix of publicly available datasets and synthetic datasets, utilizing Direct Preference Optimization (DPO).

Usage

StableLM 2 Zephyr 1.6B uses the following instruction format:

<|user|>
Which famous math number begins with 1.6 ...?<|endoftext|>
<|assistant|>
The number you are referring to is 1.618033988749895. This is the famous value known as the golden ratio<|endoftext|>

This format is also available through the tokenizer's apply_chat_template method:

from transformers import AutoModelForCausalLM, AutoTokenizer

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

prompt = [{'role': 'user', 'content': 'Which famous math number begins with 1.6 ...?'}]
inputs = tokenizer.apply_chat_template(
    prompt,
    add_generation_prompt=True,
    return_tensors='pt'
)

tokens = model.generate(
    inputs.to(model.device),
    max_new_tokens=1024,
    temperature=0.5,
    do_sample=True
)

print(tokenizer.decode(tokens[0], skip_special_tokens=False))

Model Details

Developed by: Stability AI
Model type: StableLM 2 Zephyr 1.6B model is an auto-regressive language model based on the transformer decoder architecture.
Language(s): English
Paper: Stable LM 2 1.6B Technical Report
Library: Alignment Handbook
Finetuned from model: https://huggingface.co/stabilityai/stablelm-2-1_6b
License: StabilityAI Non-Commercial Research Community License. If you want to use this model for your commercial products or purposes, please contact us here to learn more.
Contact: For questions and comments about the model, please email lm@stability.ai

Training Dataset

The dataset is comprised of a mixture of open datasets large-scale datasets available on the HuggingFace Hub:

SFT Datasets

HuggingFaceH4/ultrachat_200k
meta-math/MetaMathQA
WizardLM/WizardLM_evol_instruct_V2_196k
Open-Orca/SlimOrca
openchat/openchat_sharegpt4_dataset
LDJnr/Capybara
hkust-nlp/deita-10k-v0

Preference Datasets:

allenai/ultrafeedback_binarized_cleaned
Intel/orca_dpo_pairs

Performance

MT-Bench

Model	Size	MT-Bench
Mistral-7B-Instruct-v0.2	7B	7.61
Llama2-Chat	70B	6.86
stablelm-zephyr-3b	3B	6.64
MPT-30B-Chat	30B	6.39
stablelm-2-zephyr-1.6b	1.6B	5.42
Falcon-40B-Instruct	40B	5.17
Qwen-1.8B-Chat	1.8B	4.95
dolphin-2.6-phi-2	2.7B	4.93
phi-2	2.7B	4.29
TinyLlama-1.1B-Chat-v1.0	1.1B	3.46

OpenLLM Leaderboard

Model	Size	Average	ARC Challenge (acc_norm)	HellaSwag (acc_norm)	MMLU (acc_norm)	TruthfulQA (mc2)	Winogrande (acc)	Gsm8k (acc)
microsoft/phi-2	2.7B	61.32%	61.09%	75.11%	58.11%	44.47%	74.35%	54.81%
stabilityai/stablelm-2-zephyr-1_6b	1.6B	49.89%	43.69%	69.34%	41.85%	45.21%	64.09%	35.18%
microsoft/phi-1_5	1.3B	47.69%	52.90%	63.79%	43.89%	40.89%	72.22%	12.43%
stabilityai/stablelm-2-1_6b	1.6B	45.54%	43.43%	70.49%	38.93%	36.65%	65.90%	17.82%
mosaicml/mpt-7b	7B	44.28%	47.70%	77.57%	30.80%	33.40%	72.14%	4.02%
KnutJaegersberg/Qwen-1_8B-Llamaified*	1.8B	44.75%	37.71%	58.87%	46.37%	39.41%	61.72%	24.41%
openlm-research/open_llama_3b_v2	3B	40.28%	40.27%	71.60%	27.12%	34.78%	67.01%	0.91%
iiuae/falcon-rw-1b	1B	37.07%	35.07%	63.56%	25.28%	35.96%	62.04%	0.53%
TinyLlama/TinyLlama-1.1B-3T	1.1B	36.40%	33.79%	60.31%	26.04%	37.32%	59.51%	1.44%

Training Infrastructure

Hardware: StableLM 2 Zephyr 1.6B was trained on the Stability AI cluster across 8 nodes with 8 A100 80GBs GPUs for each nodes.
Code Base: We use our internal script for SFT steps and used HuggingFace Alignment Handbook script for DPO training.

Use and Limitations

Intended Use

The model is intended to be used in chat-like applications. Developers must evaluate the model for safety performance in their specific use case. Read more about safety and limitations below.

Limitations and Bias

This model is not trained against adversarial inputs. We strongly recommend pairing this model with an input and output classifier to prevent harmful responses.

Through our internal red teaming, we discovered that while the model will not output harmful information if not prompted to do so, it will hallucinate many facts. It is also willing to output potentially harmful outputs or misinformation when the user requests it. Using this model will require guardrails around your inputs and outputs to ensure that any outputs returned are not misinformation or harmful. Additionally, as each use case is unique, we recommend running your own suite of tests to ensure proper performance of this model. Finally, do not use the models if they are unsuitable for your application, or for any applications that may cause deliberate or unintentional harm to others.

How to Cite

@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}
}