---
library_name: peft
base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ
license: apache-2.0
datasets:
- nmarafo/truthful_qa_TrueFalse-Feedback
language:
- en
- es
---

# Model Card for Model ID

This is an adapter prepared to return True or False depending on whether the student's answer ("student_answer") is correct based on the question ("question") and comparing it with a given answer ("best_answer").
The prompt has the following structure:
```
<s>[INST]Analyze the question, the expected answer, and the student's response.
Determine if the student's answer is correct or not. It only returns True if the student's answer is correct with respect to the expected answer or False otherwise.
Add a brief comment explaining why the answer is correct or incorrect.\n\n
Question: {question}\n
Expected Answer: {best_answer}\n
Student Answer: {student_answer}[/INST]"
```




## Model Details

### Model Description

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- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model
In Google Colab:
'''
!pip install -q -U transformers peft accelerate optimum
!pip install datasets==2.15.0
!pip install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu117/

from peft import AutoPeftModelForCausalLM
from rich import print
from transformers import GenerationConfig, AutoTokenizer

import torch

model_id = "TheBloke/Mistral-7B-Instruct-v0.2-GPTQ"
adapter="nmarafo/Mistral-7B-Instruct-v0.2-TrueFalse-Feedback-GPTQ"

def generate_prompt(data_point):
    system_message = "Analyze the question, the expected answer, and the student's response. Determine if the student's answer is conceptually correct in relation to the expected answer, regardless of the exact wording. An answer will be considered correct if it accurately identifies the key information requested in the question, even if expressed differently. Return True if the student's answer is correct or False otherwise. Add a brief comment explaining the rationale behind the answer being correct or incorrect."
    question = data_point["question"][0]
    best_answer = data_point["best_answer"][0]
    student_answer = data_point["student_answer"][0]
    prompt = f"{system_message}\n\nQuestion: {question}\nExpected Answer: {best_answer}\nStudent Answer: {student_answer}"

    return prompt

tokenizer = AutoTokenizer.from_pretrained(
            model_id, 
            trust_remote_code=True, 
            return_token_type_ids=False)
tokenizer.pad_token = tokenizer.eos_token

question="Name of Canary Island"
best_answer="Tenerife, Fuerteventura, Gran Canaria, Lanzarote, La Palma, La Gomera, El Hierro, La Graciosa"
student_answer="Tenerife"

prompt = generate_prompt([{"question":question, "best_answer":best_answer,"student_answer":student_answer}])
prompt_template=f'''<s>[INST] {prompt} [/INST]'''

input_ids = tokenizer(prompt, return_tensors='pt').input_ids.cuda()
output = persisted_model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))    

# To perform inference on the test dataset example load the model from the checkpoint
persisted_model = AutoPeftModelForCausalLM.from_pretrained(
    adapter,
    low_cpu_mem_usage=True,
    return_dict=True,
    torch_dtype=torch.float16,
    device_map="cuda")
# Some gen config knobs
generation_config = GenerationConfig(
    penalty_alpha=0.6, 
    do_sample = True, 
    top_k=5, 
    temperature=0.5, 
    repetition_penalty=1.2,
    max_new_tokens=512
)

[More Information Needed]

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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### Framework versions

- PEFT 0.8.2