TigersBots/Codingbot

Model Card for Model ID

This modelcard is tailored for a coding bot and AI assistant that can perform complex code generation, code completion, and text summarization. It has been trained on large code and text corpora for improved accuracy in NLP and code-related tasks.

Model Details

Model Description

This model is designed for high-performance programming and natural language processing. It is fine-tuned on a wide range of coding tasks (like code completion and generation) and general-purpose NLP tasks (like text summarization and conversation generation).

• Developed by: DeepSeek AI, Mistral AI, BigCode, Facebook, and BigScience
• Funded by: [More Information Needed]
• Shared by: [More Information Needed]
• Model type: Code Generation and NLP
• Language(s) (NLP): English, Programming Languages (Python, JavaScript, etc.)
• License: MIT
• Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

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

Uses

Direct Use

This model is ideal for:

• Code generation (e.g., writing scripts, functions)
• Text summarization
• Text generation (e.g., conversations, code comments)

Downstream Use [optional]

It can be fine-tuned further for specific tasks such as:

• Chatbots (via conversational AI integration)
• IDE integrations for code assistance
• Content creation tools (e.g., blog posts, articles)

Out-of-Scope Use

This model should not be used for:

• Generating harmful or malicious content
• Tasks involving highly sensitive data that require extra privacy measures

Bias, Risks, and Limitations

The model may exhibit some biases depending on the datasets it was trained on. It is crucial to ensure that the outputs are validated, especially in sensitive areas like healthcare, finance, etc.

Recommendations

Users should be mindful of potential biases and limitations, and it’s recommended to validate outputs in critical use cases.

How to Get Started with the Model

Use the following code to get started with the model:

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1")
model = AutoModelForSequenceClassification.from_pretrained("deepseek-ai/DeepSeek-R1")

inputs = tokenizer("Here is some text to process", return_tensors="pt")
outputs = model(**inputs)