| --- |
| license: mit |
| language: |
| - en |
| base_model: |
| - gpt-4o-2024-08-06-codette |
| - Raiff1982/coder |
| - Raiff1982/Codette |
| library_name: adapter-transformers |
| datasets: |
| - Raiff1982/coredata |
| - Raiff1982/pineco |
| metrics: |
| - code_eval |
| - bleurt |
| - bleu |
| - accuracy |
| - bertscore |
| - brier_score |
| tags: |
| - code |
| - chemistry |
| - legal |
| - climate |
| pipeline_tag: question-answering |
| new_version: Raiff1982/deepercodette |
| --- |
| |
| # Model Card for Model ID |
|
|
| <!-- Provide a quick summary of what the model is/does. --> |
|
|
| This model card aims to be a base template for new models. |
|
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| ## Model Details |
|
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| ### Model Description |
|
|
| <!-- Provide a longer summary of what this model is. --> |
|
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| 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](https://www.office.com/search?q=Jonathan+Harrison&EntityRepresentationId=cbf3097b-72bf-4444-952d-1e473728191f) |
| - **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 |
|
|
| <!-- Provide the basic links for the model. --> |
|
|
| - **Repository:** The model's code and configuration files can be found in the readme |
| - **Paper [optional]:** [More Information Needed] |
| - **Demo [optional]:** |
|
|
| ## Uses |
|
|
| <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
|
|
| ### 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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| This model can be used directly for question-answering tasks, providing accurate and relevant answers based on the input queries. |
|
|
| ### Downstream Use [optional] |
|
|
| <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
|
|
| The model can be fine-tuned for specific tasks or integrated into larger systems to enhance its capabilities and performance. |
|
|
| ### Out-of-Scope Use |
|
|
| <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
|
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| 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 |
|
|
| <!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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| 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 |
|
|
| <!-- 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 is needed for further recommendations. |
|
|
| ## How to Get Started with the Model |
|
|
| Use the code below to get started with the model. |
|
|
| ```python |
| 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 |
|
|
| <!-- 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. --> |
|
|
| 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 |
| |
| <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the 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] |
| |
| <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
| |
| 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 |
| |
| <!-- This section describes the evaluation protocols and provides the results. --> |
| |
| ### Testing Data, Factors & Metrics |
| |
| #### Testing Data |
| |
| <!-- This should link to a Dataset Card if possible. --> |
| |
| The model was tested on a diverse set of question-answering benchmarks to evaluate its performance across different domains and query types. |
| |
| #### Factors |
| |
| <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
| |
| The evaluation considered factors such as query complexity, domain specificity, and linguistic variations. |
| |
| #### Metrics |
| |
| <!-- These are the evaluation metrics being used, ideally with a description of why. --> |
| |
| The model has been evaluated using metrics such as character, accuracy, bertscore, code_eval, brier_score, bleu, and bleurt. |
| |
| ### Results |
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| 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] |
| |
| <!-- Relevant interpretability work for the model goes here --> |
| |
| The model's interpretability was assessed through attention visualization and feature importance analysis, providing insights into its decision-making process. |
| |
| ## Environmental Impact |
| |
| <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
| |
| 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 |
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| 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 |
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| The training and evaluation were conducted on a high-performance computing cluster with NVIDIA A100 GPUs. |
| |
| #### Hardware |
| |
| NVIDIA A100 GPUs |
| |
| #### Software |
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| The model was developed using Python, TensorFlow, and PyTorch frameworks. |
| |
| ## Citation [optional] |
| |
| <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
| |
| **BibTeX:** |
| |
| ```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] |
| |
| <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
| |
| - **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 jonathan@raiffsbits.com. |
| |
| --- |
