metadata

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
language:
  - en
tags:
  - causal-lm
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
license: other

`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

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-chat', trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    'stabilityai/stablelm-2-chat',
    device_map="auto",
    trust_remote_code=True,
)

prompt = [{'role': 'user', 'content': 'How to achieve multiple rows of data into one row of data in Excel?'}]
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.input_ids.shape[-1]:][0], skip_special_tokens=False)

print(output)

StableLM 2 12B Chat also supports function call usage this is an example how you can use it:

system_prompt_func = """\
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 able to creating, drawing, or illustrating an image from a text prompt.",
      "parameters": {
        "type": "object",
        "properties": {
          "prompt": {
            "type": "string",
            "description": "The description of image that user wanto to create."
          }
        },
        "required": [
          "prompt"
        ]
      }
    }
  }
]
"""
messages = [
    {'role': 'system', 'content': system_prompt},
    {'role': "user", 'content': "Help me to generate a picture of Eiffel Tower in the 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.input_ids.shape[-1]:][0], skip_special_tokens=False)

print(output)

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:

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:

Performance

MT-Bench

OpenLLM Leaderboard

Training Infrastructure

Hardware: StableLM 2 12B Chat 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 training and HuggingFace Alignment Handbook 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.

stabilityai
/

stablelm-2-12b-chat