aquila-135m-csqa

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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

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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.

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 dataset.

Training Procedure

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Preprocessing [optional]

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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 -->

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Results

Summary

Model Examination [optional]

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Citation [optional]

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Glossary [optional]

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