abdullah1027/mistral-7b-instruct-finetuned-maleeha-lodhi-style

Maleeha Lodhi Style Mistral Instruct

This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.3 capable of generating opinion-style text in the manner of Pakistani diplomat, journalist, and political scientist, Maleeha Lodhi. It has been adapted to mimic her distinctive writing style and analytical approach to current affairs.

Model Details

Model Description

This is a custom-trained language model, fine-tuned using the Parameter-Efficient Fine-Tuning (PEFT) method, specifically LoRA (Low-Rank Adaptation), on a specialized dataset. The objective was to adapt the pre-trained Mistral-7B-Instruct-v0.3 model to generate coherent and stylistically consistent text resembling the opinion pieces of Maleeha Lodhi, based on articles scraped from Dawn News.

The base model, Mistral-7B-Instruct-v0.3, is a powerful 7-billion parameter, decoder-only transformer model designed for instruction following. This fine-tuning process leverages its existing capabilities and refines them for a more specific generation style.

• Developed by: Abdullah Usama
• Model type: Causal Language Model (Transformer, Decoder-Only), fine-tuned with LoRA.
• Language(s) (NLP): English
• License: Apache 2.0 (Inherited from Mistral-7B-Instruct-v0.3)
• Finetuned from model: mistralai/Mistral-7B-Instruct-v0.3

Uses

Direct Use

This model is intended for text generation tasks where the desired output should emulate the analytical and opinionated writing style of Maleeha Lodhi, particularly on topics related to current affairs, international relations, and Pakistani politics.

Example applications include:

• Generating opinion drafts or outlines.
• Creative writing exercises in a specific journalistic style.
• As a research tool to explore different perspectives on current events (caution advised regarding factual accuracy – see 'Bias, Risks, and Limitations').

Downstream Use

This model can be integrated into larger applications requiring stylistically specific text generation, such as:

• Automated content creation pipelines for journalistic analysis (with human oversight).
• Educational tools for studying rhetorical styles.

Out-of-Scope Use

This model is not intended for:

• Generating factual news reports without human verification.
• Providing legal, financial, or medical advice.
• Generating harmful, unethical, or malicious content.
• Any use that violates the Apache 2.0 license or the ethical guidelines for AI.

Bias, Risks, and Limitations

As a language model fine-tuned on specific content, this model inherits biases present in its training data (both the base Mistral model's vast corpus and the scraped Maleeha Lodhi articles). Potential biases include:

• Political/Ideological Bias: The model's output will likely reflect the political leanings, perspectives, and biases inherent in Maleeha Lodhi's writings and the general discourse of Dawn News. It may implicitly favor certain viewpoints or interpretations of events.
• Domain Specificity: While trained on general current affairs, its style is specific. It may perform less optimally on topics outside the scope of Maleeha Lodhi's typical subject matter.
• Factual Accuracy: The model is designed to generate text stylistically. It does not guarantee factual accuracy or up-to-date information. Users must independently verify any factual claims made by the model.
• Language Nuance: While trained in English, subtle nuances or cultural contexts unique to Pakistani discourse may be overemphasized or misrepresented.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.

• Always verify factual content: Treat generated text as a starting point, not as definitive truth.
• Be aware of inherent biases: Recognize that the model's opinions and interpretations are reflections of its training data, not objective truth.
• Avoid sensitive applications: Do not use this model for applications where accuracy, neutrality, or a lack of bias is paramount (e.g., critical decision-making, sensitive political commentary without review).
• Human-in-the-loop: For any critical application, maintain a human-in-the-loop to review, edit, and validate generated content.