---
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

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- **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

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### 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 for our method is 0.064 (out of 1) and is consistently low across tasks (maximum task ECE 0.10), compared to the base model average ECE of 0.20 and maximum task ECE of 0.60. Note that our ECE of 0.064 is smaller than the gap between the quantized certainty outputs (10% quantization steps). Additionally, the zero-shot performance on the MMLU tasks does not degrade, averaging at 89%.
<!-- This section describes the evaluation protocols and provides the results. -->



![image/png](https://cdn-uploads.huggingface.co/production/uploads/6602ffd971410cf02bf42c06/2MwP7DRZlNBtWSKWFvXOI.png)


## Model Card Authors 

Kristjan Greenewald