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
lm@stability.ai
.
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}
}
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