aquila-135m-csqa/README.md

library_name, tags, datasets, base_model, license

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

Model Card for Model ID

Model Details

Model Description

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]

• Repository: [More Information Needed]
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

Direct Use

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

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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

The model was trained on the tau/commonsense_qa dataset.

Training Procedure

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

Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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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]

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Environmental Impact

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

• 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]

BibTeX:

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APA:

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Glossary [optional]

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Model Card Authors [optional]

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Model Card Contact

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