Text Generation
Transformers
Safetensors
English
stablelm
causal-lm
conversational
Inference Endpoints
Edit model card

StableLM 2 12B Chat

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).

Usage

NOTE: This model requires transformers>=4.40.0

StableLM 2 12B Chat uses the following instruction ChatML format. This format is also available through the tokenizer's apply_chat_template method:

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('stabilityai/stablelm-2-12b-chat')
model = AutoModelForCausalLM.from_pretrained(
    'stabilityai/stablelm-2-12b-chat',
    device_map="auto",
)

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

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

print(output)

StableLM 2 12B Chat also supports function calling. The following is an example of how to use it:

system_prompt = """\
You are a helpful assistant with access to the following functions. You must use them if required -\n
[
  {
    "type": "function",
    "function": {
      "name": "TextToImage",
      "description": "This function is able to create, draw, or illustrate an image from a text prompt.",
      "parameters": {
        "type": "object",
        "properties": {
          "prompt": {
            "type": "string",
            "description": "The description of image that the user wants to create."
          }
        },
        "required": [
          "prompt"
        ]
      }
    }
  }
]
"""
messages = [
    {'role': 'system', 'content': system_prompt},
    {'role': "user", 'content': "Please, generate a picture of the Eiffel Tower at night!"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors='pt'
)

tokens = model.generate(
    inputs.to(model.device),
    max_new_tokens=1024,
    temperature=0.5,
    do_sample=True
)
output = tokenizer.decode(tokens[:, inputs.shape[-1]:][0], skip_special_tokens=True)

print(output)
"""
[
  {
    "name": "TextToImage",
    "arguments": {
      "prompt": "Eiffel Tower at night."
    }
  }
]
"""

Model Details

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:

  1. 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
  1. Safety Datasets:
  • Anthropic/hh-rlhf
  • Internal Safety Dataset
  1. 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 Β± 0.06
stabilityai/stablelm-2-12b-chat 12B 8.15 Β± 0.08
Qwen/Qwen1.5-14B-Chat 14B 7.95 Β± 0.10
HuggingFaceH4/zephyr-7b-gemma-v0.1 8.5B 7.82 Β± 0.03
mistralai/Mistral-7B-Instruct-v0.2 7B 7.48 Β± 0.02
meta-llama/Llama-2-70b-chat-hf 70B 7.29 Β± 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}
}
Downloads last month
5,668
Safetensors
Model size
12.1B params
Tensor type
BF16
Β·

Datasets used to train stabilityai/stablelm-2-12b-chat

Spaces using stabilityai/stablelm-2-12b-chat 4

Collection including stabilityai/stablelm-2-12b-chat