README.md

---
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

<!-- Provide a longer summary of what this model is. -->



- **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

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

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## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### 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"

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

model = AutoPeftModelForCausalLM.from_pretrained(adapter, low_cpu_mem_usage=True, return_dict=True, torch_dtype=torch.float16, device_map="cuda")

def predict(question, best_answer, student_answer):
    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. 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."
    prompt = f"{system_message}\n\nQuestion: {question}\nBest Answer: {best_answer}\nStudent Answer: {student_answer}"
    prompt_template=f"<s>[INST]{prompt}[/INST]"

    encoding = tokenizer(prompt_template, return_tensors='pt', padding=True, truncation=True, max_length=512)
    input_ids = encoding['input_ids'].cuda()
    attention_mask = encoding['attention_mask'].cuda()

    output = model.generate(input_ids, attention_mask=attention_mask, 
                            temperature=0.7, do_sample=True, top_p=0.95, 
                            top_k=40, max_new_tokens=512, pad_token_id=tokenizer.eos_token_id)
    response = tokenizer.decode(output[0], skip_special_tokens=True)
    return response

question="Mention all the Canary Island"
best_answer="Tenerife, Fuerteventura, Gran Canaria, Lanzarote, La Palma, La Gomera, El Hierro, La Graciosa"
student_answer="Tenerife"

print(predict(question, best_answer, student_answer))    

```

# 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]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

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

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

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

[More Information Needed]

## 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

[More Information Needed]

### 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]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

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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