RinKana

aquila-135m-csqa

README

License: apache-2.0

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

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

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.

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

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]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

Table with columns: Epoch, Training Loss, Validation Loss, Entropy, Num Tokens, Mean Token Accuracy
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

Summary

Model Examination [optional]

[More Information Needed]

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

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

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Model Card Contact

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Model Details

Model Provider

RinKana

Model Tree

Base

BAAI/Aquila-135M-Instruct

Fine-tuned

this model

Input Modalities

Text

Output Modalities

Text

Supported Functionality

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README

License: apache-2.0

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

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

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.

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

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]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

Table with columns: Epoch, Training Loss, Validation Loss, Entropy, Num Tokens, Mean Token Accuracy
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

Summary

Model Examination [optional]

[More Information Needed]

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

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]