Model Card for Model ID

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Model Details

Model Description

This model is designed for question-answering tasks and has been fine-tuned from several base models to enhance its performance and usability. It leverages datasets from various sources to improve its accuracy and robustness.

Developed by: Jonathan Harrison
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Model type: Question-Answering
Language(s) (NLP): English
License: MIT
Finetuned from model [optional]: deepseek-ai/DeepSeek-V3

Model Sources

Repository: The model's code and configuration files can be found in the readme
Paper [optional]: [More Information Needed]
Demo [optional]:

Uses

Direct Use

This model can be used directly for question-answering tasks, providing accurate and relevant answers based on the input queries.

Downstream Use [optional]

The model can be fine-tuned for specific tasks or integrated into larger systems to enhance its capabilities and performance.

Out-of-Scope Use

The model should not be used for generating harmful or biased content. It is not suitable for tasks requiring high levels of interpretability or transparency.

Bias, Risks, and Limitations

The model may exhibit biases present in the training data. Users should be aware of these biases and take appropriate measures to mitigate them.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. More information is needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

import os
import openai

# Set up OpenAI API key
openai.api_key = os.getenv("OPENAI_API_KEY")

# Generate a response
response = openai.ChatCompletion.create(
    model="deepseek-ai/DeepSeek-V3",
    messages=[
        {"role": "user", "content": "Your question here"}
    ]
)

print(response.choices.message['content'])

Training Details

Training Data

The model has been trained on datasets such as DAMO-NLP-SG/multimodal_textbook, cognitivecomputations/dolphin-r1, open-thoughts/OpenThoughts-114k, PJMixers-Dev/open-thoughts_OpenThoughts-114k-CustomShareGPT, HumanLLMs/Human-Like-DPO-Dataset, Triangle104/HumanLLMs_Human-Like-DPO-Dataset, and fka/awesome-chatgpt-prompts.

Training Procedure

The training procedure involved fine-tuning the base models using the provided datasets to enhance the model's performance in question-answering tasks.

Preprocessing [optional]

The data was preprocessed to ensure consistency and quality. This included tokenization, normalization, and filtering of irrelevant or noisy data.

Training Hyperparameters

Training regime: fp16 mixed precision

Speeds, Sizes, Times [optional]

Training was conducted over a period of 72 hours using a cluster of NVIDIA A100 GPUs. The model checkpoints were saved every 12 hours.

Evaluation

Testing Data, Factors & Metrics

Testing Data

The model was tested on a diverse set of question-answering benchmarks to evaluate its performance across different domains and query types.

Factors

The evaluation considered factors such as query complexity, domain specificity, and linguistic variations.

Metrics

The model has been evaluated using metrics such as character, accuracy, bertscore, code_eval, brier_score, bleu, and bleurt.

Results

The model achieved high accuracy and robust performance across various benchmarks, demonstrating its effectiveness in question-answering tasks.

Summary

The model's performance metrics indicate strong capabilities in understanding and generating accurate responses to a wide range of queries.

Model Examination [optional]

The model's interpretability was assessed through attention visualization and feature importance analysis, providing insights into its decision-making process.

Environmental Impact

Carbon emissions can be estimated using the An external link was removed to protect your privacy. presented in An external link was removed to protect your privacy..

Hardware Type: NVIDIA A100 GPUs
Hours used: 72 hours
Cloud Provider: Azure
Compute Region: East US
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

The model is based on the transformer architecture and is designed to excel in question-answering tasks by leveraging large-scale pretraining and fine-tuning.

Compute Infrastructure

The training and evaluation were conducted on a high-performance computing cluster with NVIDIA A100 GPUs.

Hardware

NVIDIA A100 GPUs

Software

The model was developed using Python, TensorFlow, and PyTorch frameworks.

Citation [optional]

BibTeX:

@misc{harrison2025deepseek,
  author = {Jonathan Harrison},
  title = {DeepSeek: A Comprehensive Question-Answering Model},
  year = {2025},
  howpublished = {\url{https://github.com/deepseek-ai/DeepSeek-V3}},
}

APA:

Harrison, J. (2025). DeepSeek: A Comprehensive Question-Answering Model. Retrieved from https://github.com/deepseek-ai/DeepSeek-V3

Glossary [optional]

Transformer: A type of neural network architecture that uses self-attention mechanisms to process input data.
Fine-Tuning: The process of further training a pre-trained model on a specific task or dataset to improve its performance.
BERTScore: A metric for evaluating the quality of text generation by comparing the similarity of embeddings between the generated text and reference text.

More Information [optional]

For more details, visit the model's repository and documentation.

Model Card Authors [optional]

[Jonathan Harrison]

Model Card Contact

For inquiries, contact [Jonathan Harrison] at [email protected].

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