---
library_name: transformers
tags:
- trl
- sft
datasets:
- tau/commonsense_qa
base_model:
- BAAI/Aquila-135M-Instruct
license: apache-2.0
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** Chakrabhuana Vishnu Deva
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** Causal Language Model
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model [optional]:** BAAI/Aquila-135M-Instruct

### 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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The model is designed to answer multiple-choice questions. It is fine-tuned on the `tau/commonsense_qa` dataset.

### 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

Use the code below to get started with the model.

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "RinKana/aquila-135m-csqa" # Note: replace with your exact repo ID if different

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, device_map="auto")

question = "What is the capital of France?"
options = "A. London\nB. Paris\nC. Berlin\nD. Madrid\nE. Rome"

user_prompt = (
    "Answer the following multiple-choice question.\n\n"
    f"Question: {question}\n"
    f"Options:\n{options}\n\n"
    "Answer with ONLY the letter (A, B, C, D, or E)."
)

messages = [{"role": "user", "content": user_prompt}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=5,
        temperature=0.0,
        do_sample=False,
    )

input_length = inputs["input_ids"].shape[1]
response = tokenizer.decode(outputs[0][input_length:], skip_special_tokens=True).strip()
print(f"Predicted Answer: {response}")
```

## Training Details

### Training Data

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The model was trained on the [tau/commonsense_qa](https://huggingface.co/datasets/tau/commonsense_qa) dataset.

### Training Procedure

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#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

The model was trained with the following hyperparameters:
- **max_length:** 256
- **per_device_train_batch_size:** 16
- **per_device_eval_batch_size:** 16
- **gradient_accumulation_steps:** 2
- **num_train_epochs:** 3
- **learning_rate:** 2e-5
- **Training regime:** bf16 mixed precision <!--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

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### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

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#### Metrics

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[More Information Needed]

### Results

| Epoch | Training Loss | Validation Loss | Entropy | Num Tokens | Mean Token Accuracy |
|---|---|---|---|---|---|
| 1 | 1.164672 | 1.156044 | 1.286664 | 864431.000000 | 0.786455 |
| 2 | 1.125324 | 1.123461 | 1.223981 | 1728862.000000 | 0.798904 |
| 3 | 1.110953 | 1.120729 | 1.214477 | 2593293.000000 | 0.798789 |

#### Summary



## Model Examination [optional]

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

[More Information Needed]

## Environmental Impact

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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]

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**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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