metadata

language:
  - en
license: other
tags:
  - causal-lm
datasets:
  - HuggingFaceH4/ultrachat_200k
  - allenai/ultrafeedback_binarized_cleaned
  - meta-math/MetaMathQA
  - WizardLM/WizardLM_evol_instruct_V2_196k
  - openchat/openchat_sharegpt4_dataset
  - LDJnr/Capybara
  - Intel/orca_dpo_pairs
  - hkust-nlp/deita-10k-v0
  - Anthropic/hh-rlhf
  - glaiveai/glaive-function-calling-v2
extra_gated_fields:
  Name: text
  Email: text
  Country: text
  Organization or Affiliation: text
  I ALLOW Stability AI to email me about new model releases: checkbox

`StableLM 2 12B Chat GGUF`

This repository contains GGUF format files for StableLM 2 12B Chat. Files were generated with the b2684 llama.cpp release.

Model Description

Stable LM 2 12B Chat is a 12 billion parameter instruction tuned language model trained on a mix of publicly available datasets and synthetic datasets, utilizing Direct Preference Optimization (DPO).

Example Usage via `llama.cpp`

Make sure to install release b2684 or later.

Download any of the available GGUF files. For example, using the Hugging Face Hub CLI:

pip install huggingface_hub[hf_transfer]
export HF_HUB_ENABLE_HF_TRANSFER=1
huggingface-cli download stabilityai/stablelm-2-12b-chat-GGUF stablelm-2-12b-chat-Q5_K_M.gguf --local-dir . --local-dir-use-symlinks False

Then run the model with the llama.cpp main program:

./main -m stablelm-2-12b-chat-Q5_K_M.gguf -p "<|im_start|>user {PROMPT} <|im_end|><|im_start|>assistant"

For interactive conversations, make sure to use ChatML formatting via the -cml flag:

./main -m stablelm-2-12b-chat-Q5_K_M.gguf -p {SYSTEM_PROMPT} -cml

Model Details

Developed by: Stability AI
Model type: StableLM 2 12B Chat model is an auto-regressive language model based on the transformer decoder architecture.
Language(s): English
Paper: Stable LM 2 Chat Technical Report
Library: Alignment Handbook
Finetuned from model:
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 as well as an internal safety dataset:

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
teknium/OpenHermes-2.5
glaiveai/glaive-function-calling-v2

Safety Datasets:

Anthropic/hh-rlhf
Internal Safety Dataset

Preference Datasets:

argilla/dpo-mix-7k

Performance

MT-Bench

Model	Parameters	MT Bench (Inflection-corrected)
mistralai/Mixtral-8x7B-Instruct-v0.1	13B/47B	8.48 ± 0.06
stabilityai/stablelm-2-12b-chat	12B	8.15 ± 0.08
Qwen/Qwen1.5-14B-Chat	14B	7.95 ± 0.10
HuggingFaceH4/zephyr-7b-gemma-v0.1	8.5B	7.82 ± 0.03
mistralai/Mistral-7B-Instruct-v0.2	7B	7.48 ± 0.02
meta-llama/Llama-2-70b-chat-hf	70B	7.29 ± 0.05

OpenLLM Leaderboard

Model	Parameters	Average	ARC Challenge (25-shot)	HellaSwag (10-shot)	MMLU (5-shot)	TruthfulQA (0-shot)	Winogrande (5-shot)	GSM8K (5-shot)
mistralai/Mixtral-8x7B-Instruct-v0.1	13B/47B	72.71	70.14	87.55	71.40	64.98	81.06	61.11
stabilityai/stablelm-2-12b-chat	12B	68.45	65.02	86.06	61.14	62.00	78.77	57.70
Qwen/Qwen1.5-14B	14B	66.70	56.57	81.08	69.36	52.06	73.48	67.63
mistralai/Mistral-7B-Instruct-v0.2	7B	65.71	63.14	84.88	60.78	60.26	77.19	40.03
HuggingFaceH4/zephyr-7b-gemma-v0.1	8.5B	62.41	58.45	83.48	60.68	52.07	74.19	45.56
Qwen/Qwen1.5-14B-Chat	14B	62.37	58.79	82.33	68.52	60.38	73.32	30.86
google/gemma-7b	8.5B	63.75	61.09	82.20	64.56	44.79	79.01	50.87
stabilityai/stablelm-2-12b	12B	63.53	58.45	84.33	62.09	48.16	78.10	56.03
mistralai/Mistral-7B-v0.1	7B	60.97	59.98	83.31	64.16	42.15	78.37	37.83
meta-llama/Llama-2-13b-hf	13B	55.69	59.39	82.13	55.77	37.38	76.64	22.82
meta-llama/Llama-2-13b-chat-hf	13B	54.92	59.04	81.94	54.64	41.12	74.51	15.24

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

We strongly recommend pairing this model with an input and output classifier to prevent harmful responses. Using this model will require guardrails around your inputs and outputs to ensure that any outputs returned are not hallucinations. 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

@article{bellagente2024stable,
  title={Stable LM 2 1.6 B Technical Report},
  author={Bellagente, Marco and Tow, Jonathan and Mahan, Dakota and Phung, Duy and Zhuravinskyi, Maksym and Adithyan, Reshinth and Baicoianu, James and Brooks, Ben and Cooper, Nathan and Datta, Ashish and others},
  journal={arXiv preprint arXiv:2402.17834},
  year={2024}
}

StableLM 2 12B Chat GGUF