---

datasets:

• HuggingFaceH4/ultrachat200k
• allenai/ultrafeedbackbinarizedcleaned
• meta-math/MetaMathQA
• WizardLM/WizardLMevolinstructV2196k
• openchat/openchatsharegpt4dataset
• LDJnr/Capybara
• Intel/orcadpo_pairs
• hkust-nlp/deita-10k-v0
• teknium/OpenHermes-2.5

language:

• en

tags:

• causal-lm

extragatedfields:
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Email: text
Country: text
Organization or Affiliation: text
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license: other

---

This is StableLM 2 Chat 1.6B, quantized with the help of imatrix so it could offer better performance for being quantized, and have quantization levels available for lower-memory devices to run. Kalomaze's "groupsmerged.txt" was used for the importance matrix, with context set to 4,096 (the context length according to their paper).

Here's a chart that provides an approximation of the HellaSwag score (out of 1,000 tasks). Thanks to the randomization of tasks, it may be slightly unprecise:

Quantization	HellaSwag
IQ1_S	35.4%
IQ1_M	38.7%
IQ2_XXS	51.2%
IQ2_XS	51.8%
IQ2_S	56.8%
IQ2_M	59.3%
Q2KS	55.2%
Q2_K	59.0%
IQ3_XXS	60.8%
Q4_0	64.0%
Q4KM	66.0%
Q5KM	65.8%

Original model card below.

---

StableLM 2 Chat 1.6B

Model Description

Stable LM 2 Chat 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 1.6B Chat uses the following ChatML format:

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.frompretrained('stabilityai/stablelm-2-16b-chat')
model = AutoModelForCausalLM.from_pretrained(
    'stabilityai/stablelm-2-1_6b-chat',
    device_map="auto",
)

prompt = [{'role': 'user', 'content': 'Implement snake game using pygame'}]
inputs = tokenizer.applychattemplate(
    prompt,
    addgenerationprompt=True,
    return_tensors='pt'
)

tokens = model.generate(
    inputs.to(model.device),
    maxnewtokens=100,
    temperature=0.7,
    do_sample=True
)
output = tokenizer.decode(tokens[:, inputs.shape[-1]:][0], skipspecialtokens=False)

print(output)

Model Details

• Developed by: Stability AI
• Model type: StableLM 2 Chat 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-2-16b
• 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 [email protected]

Training Dataset

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

1. SFT Datasets

• HuggingFaceH4/ultrachat200k
• meta-math/MetaMathQA
• WizardLM/WizardLMevolinstructV2196k
• Open-Orca/SlimOrca
• openchat/openchatsharegpt4_dataset
• LDJnr/Capybara
• hkust-nlp/deita-10k-v0
• teknium/OpenHermes-2.5

1. Preference Datasets:

• allenai/ultrafeedbackbinarizedcleaned
• Intel/orcadpopairs
• argilla/dpo-mix-7k

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-2-1_6b-chat	1.6B	5.83
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 (accnorm)	HellaSwag (accnorm)	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-2-1_6b-chat	1.6B	50.80%	43.94%	69.22%	41.59%	46.52%	64.56%	38.96%
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/openllama3b_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%

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},
      title={Stable LM 2 1.6B},
      author={Stability AI Language Team}
}