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Model: RinKana/aquila-135m-csqa Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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tags:
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- trl
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- sft
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datasets:
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- tau/commonsense_qa
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base_model:
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- BAAI/Aquila-135M-Instruct
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license: apache-2.0
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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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.
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- **Developed by:** Chakrabhuana Vishnu Deva
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** Causal Language Model
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- **Language(s) (NLP):** English
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- **License:** Apache 2.0
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- **Finetuned from model [optional]:** BAAI/Aquila-135M-Instruct
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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The model is designed to answer multiple-choice questions. It is fine-tuned on the `tau/commonsense_qa` dataset.
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_id = "RinKana/aquila-135m-csqa" # Note: replace with your exact repo ID if different
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, device_map="auto")
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question = "What is the capital of France?"
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options = "A. London\nB. Paris\nC. Berlin\nD. Madrid\nE. Rome"
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user_prompt = (
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"Answer the following multiple-choice question.\n\n"
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f"Question: {question}\n"
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f"Options:\n{options}\n\n"
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"Answer with ONLY the letter (A, B, C, D, or E)."
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)
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messages = [{"role": "user", "content": user_prompt}]
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=5,
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temperature=0.0,
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do_sample=False,
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)
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input_length = inputs["input_ids"].shape[1]
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response = tokenizer.decode(outputs[0][input_length:], skip_special_tokens=True).strip()
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print(f"Predicted Answer: {response}")
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```
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## Training Details
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### Training Data
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<!-- 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. -->
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The model was trained on the [tau/commonsense_qa](https://huggingface.co/datasets/tau/commonsense_qa) dataset.
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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The model was trained with the following hyperparameters:
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- **max_length:** 256
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- **per_device_train_batch_size:** 16
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- **per_device_eval_batch_size:** 16
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- **gradient_accumulation_steps:** 2
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- **num_train_epochs:** 3
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- **learning_rate:** 2e-5
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- **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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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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| Epoch | Training Loss | Validation Loss | Entropy | Num Tokens | Mean Token Accuracy |
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|---|---|---|---|---|---|
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| 1 | 1.164672 | 1.156044 | 1.286664 | 864431.000000 | 0.786455 |
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| 2 | 1.125324 | 1.123461 | 1.223981 | 1728862.000000 | 0.798904 |
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| 3 | 1.110953 | 1.120729 | 1.214477 | 2593293.000000 | 0.798789 |
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- 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 Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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