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---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
library_name: transformers
---
# Granite Uncertainty 3.0 8b
## Model Summary
**Granite Uncertainty 3.0 8b** is a LoRA adapter for [ibm-granite/granite-3.0-8b-instruct](https://huggingface.co/ibm-granite/granite-3.0-8b-instruct),
adding the capability to provide calibrated certainty scores when answering questions when prompted, in addition to retaining the full abilities of the [ibm-granite/granite-3.0-8b-instruct](https://huggingface.co/ibm-granite/granite-3.0-8b-instruct) model.
- **Developer:** IBM Research
- **Model type:** LoRA adapter for [ibm-granite/granite-3.0-8b-instruct](https://huggingface.co/ibm-granite/granite-3.0-8b-instruct)
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
### Model Sources
<!-- Provide the basic links for the model. -->
- **Paper:** The **Granite Uncertainty 3.0 8b** model is finetuned to provide certainty scores mimicking the output of a calibrator trained via the method in [[Shen et al. ICML 2024] Thermometer: Towards Universal Calibration for Large Language Models](https://arxiv.org/abs/2403.08819)
## Usage
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Intended use
**Granite Uncertainty 3.0 8b** is lightly tuned so that its behavior closely mimics that of [ibm-granite/granite-3.0-8b-instruct](https://huggingface.co/ibm-granite/granite-3.0-8b-instruct),
with the added ability to generate certainty scores for answers to questions when prompted.
**Certainty score definition** The model will respond with a certainty percentage, quantized to 10 possible values (i.e. 5%, 15%, 25%,...95%).
This percentage is *calibrated* in the following sense: given a set of answers assigned a certainty score of X%, approximately X% of these answers should be correct. See the eval experiment below for out-of-distribution verification of this behavior.
**Important note** Certainty is inherently an intrinsic property of a model and its abilitities. **Granite Uncertainty 3.0 8b** is not intended to predict the certainty of responses generated by any other models besides itself or [ibm-granite/granite-3.0-8b-instruct](https://huggingface.co/ibm-granite/granite-3.0-8b-instruct).
**Usage steps** Answering a question and obtaining a certainty score proceeds as follows.
1. Prompt the model with a system prompt followed by the user prompt. The model is calibrated with the system prompt below.
2. Use the model to generate a response as normal (via the `assistant` role), or insert a response from [ibm-granite/granite-3.0-8b-instruct](https://huggingface.co/ibm-granite/granite-3.0-8b-instruct).
3. Prompt the model to generate a certainty score by generating in the `certainty` role (by appending `<|start_of_role|>certainty<|end_of_role|>` and generating).
4. The model will respond with a certainty percentage, quantized with steps of 10% (i.e. 5%, 15%, 25%,...95%).
When not given the certainty generation prompt `<|start_of_role|>certainty<|end_of_role|>`, the model's behavior should mimic that of the base model [ibm-granite/granite-3.0-8b-instruct](https://huggingface.co/ibm-granite/granite-3.0-8b-instruct).
**System prompt** The model was calibrated with the following system prompt: `You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.`
It is recommended to prepend this string to any other desired system prompts.
### Quickstart Example
The following code describes how to use the Granite Uncertainty model to answer questions and obtain intrinsic calibrated certainty scores. Note that a generic system prompt is included, this is not necessary and can be modified as needed.
```python
import torch,os
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel, PeftConfig
token = os.getenv("HF_MISTRAL_TOKEN")
BASE_NAME = "ibm-granite/granite-3.0-8b-instruct"
LORA_NAME = "ibm-granite/granite-uncertainty-3.0-8b-lora"
device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Load model
token = os.getenv("HF_MISTRAL_TOKEN")
tokenizer = AutoTokenizer.from_pretrained(BASE_NAME,padding_side='left',trust_remote_code=True, token=token)
model_base = AutoModelForCausalLM.from_pretrained(BASE_NAME,device_map="auto")
model_UQ = PeftModel.from_pretrained(model_base, LORA_NAME)
system_prompt = "You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior."
question = "What is IBM?"
print("Question:" + question)
question_chat = [
{
"role": "system",
"content": system_prompt
},
{
"role": "user",
"content": question
},
]
# Generate answer
input_text = tokenizer.apply_chat_template(question_chat,tokenize=False,add_generation_prompt=True)
inputs = tokenizer(input_text, return_tensors="pt")
output = model_UQ.generate(inputs["input_ids"].to(device), attention_mask=inputs["attention_mask"].to(device), max_new_tokens=80)
output_text = tokenizer.decode(output[0])
answer = output_text.split("assistant<|end_of_role|>")[1]
print("Answer: " + answer)
# Generate certainty score
uq_generation_prompt = "<|start_of_role|>certainty<|end_of_role|>"
uq_chat = [
{
"role": "system",
"content": system_prompt
},
{
"role": "user",
"content": question
},
{
"role": "assistant",
"content": answer
},
]
uq_text = tokenizer.apply_chat_template(uq_chat,tokenize=False) + uq_generation_prompt
inputs = tokenizer(uq_text, return_tensors="pt")
output = model_UQ.generate(inputs["input_ids"].to(device), attention_mask=inputs["attention_mask"].to(device), max_new_tokens=1)
output_text = tokenizer.decode(output[0])
uq_score = int(output_text[-1])
print("Certainty: " + str(5 + uq_score * 10) + "%")
```
## Training Details
The **Granite Uncertainty 3.0 8b** model is a LoRA adapter finetuned to provide certainty scores mimicking the output of a calibrator trained via the method in [[Shen et al. ICML 2024] Thermometer: Towards Universal Calibration for Large Language Models](https://arxiv.org/abs/2403.08819).
### Training Data
The following datasets were used for calibration and/or finetuning.
* [BigBench](https://huggingface.co/datasets/tasksource/bigbench)
* [MRQA](https://huggingface.co/datasets/mrqa-workshop/mrqa)
* [newsqa](https://huggingface.co/datasets/lucadiliello/newsqa)
* [trivia_qa](https://huggingface.co/datasets/mandarjoshi/trivia_qa)
* [search_qa](https://huggingface.co/datasets/lucadiliello/searchqa)
* [openbookqa](https://huggingface.co/datasets/allenai/openbookqa)
* [web_questions](https://huggingface.co/datasets/Stanford/web_questions)
* [smiles-qa](https://huggingface.co/datasets/alxfgh/ChEMBL_Drug_Instruction_Tuning)
* [orca-math](https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k)
* [ARC-Easy](https://huggingface.co/datasets/allenai/ai2_arc)
* [commonsense_qa](https://huggingface.co/datasets/tau/commonsense_qa)
* [social_i_qa](https://huggingface.co/datasets/allenai/social_i_qa)
* [super_glue](https://huggingface.co/datasets/aps/super_glue)
* [figqa](https://huggingface.co/datasets/nightingal3/fig-qa)
* [riddle_sense](https://huggingface.co/datasets/INK-USC/riddle_sense)
* [ag_news](https://huggingface.co/datasets/fancyzhx/ag_news)
* [medmcqa](https://huggingface.co/datasets/openlifescienceai/medmcqa)
* [dream](https://huggingface.co/datasets/dataset-org/dream)
* [codah](https://huggingface.co/datasets/jaredfern/codah)
* [piqa](https://huggingface.co/datasets/ybisk/piqa)
## Evaluation
The model was evaluated on the [MMLU](https://huggingface.co/datasets/cais/mmlu) datasets (not used in training). Shown are the [Expected Calibration Error (ECE)](https://towardsdatascience.com/expected-calibration-error-ece-a-step-by-step-visual-explanation-with-python-code-c3e9aa12937d) for each task, for the base model (Granite-3.0-8b-instruct) and Granite-Uncertainty-3.0-8b.
The average ECE across tasks is 0.06 (out of 1). Note that this is smaller than the gap between the quantized certainty outputs (10% quantization steps).
<!-- This section describes the evaluation protocols and provides the results. -->
## Model Card Authors
Kristjan Greenewald |