Qwen/Qwen3-4B-Base Fashion Exclusion Inference Model
Model Overview
This model is a fine-tuned version of Qwen/Qwen3-4B-Base designed to identify incompatible fashion styles based on a user's profile. It uses QLoRA 4-bit fine-tuning for efficient training and memory usage. The model predicts negative style exclusions based on user preferences, helping recommendation systems avoid incompatible fashion styles.
Model Details
Model Description
This model is fine-tuned on a dataset that includes user preferences and lifestyle information, inferring 2–3 fashion styles that do not fit the user's profile. It assigns a Confidence Score (High, Medium, Low) to each exclusion and provides reasoning for each style exclusion.
- Fine-tuned from model: Qwen/Qwen3-4B-Base
Model Sources
Uses
Direct Use
This model is intended to be used for filtering fashion styles based on user profiles, helping fashion recommendation systems identify styles that are incompatible with the user.
Downstream Use
This model can be integrated into larger fashion recommendation systems or style analysis tools, where it can continuously learn and adapt to user preferences and trends.
Out-of-Scope Use
This model should not be used for generating new fashion styles or recommending outfits. It is strictly for identifying negative style exclusions based on user profiles.
Bias, Risks, and Limitations
Bias
The model may exhibit bias based on the training data it was fine-tuned on, particularly if the dataset does not account for a diverse range of body types, cultural backgrounds, and fashion preferences. Special care should be taken to monitor for fairness and inclusivity in the results.
Risks
This model is designed to exclude fashion styles based on user profiles. However, exclusion recommendations may not always align with the user’s actual preferences or societal fashion trends, especially for users whose profiles are underrepresented.
Recommendations
Users should be made aware of the limitations of this model. It is essential to balance user exclusions with positive recommendations to maintain an inclusive and diverse experience.
How to Get Started with the Model
Use the code below to load the model and generate fashion style exclusions.
1from transformers import AutoTokenizer, AutoModelForCausalLM
2
3# Load the model and tokenizer
4model_name = "Devikshah/qwen3-4b-qlora-output"
5tokenizer = AutoTokenizer.from_pretrained(model_name)
6model = AutoModelForCausalLM.from_pretrained(model_name)
7
8# Example input profile
9user_profile = "Gender: Male, Prefers structured outfits, Corporate executive, Dislikes bold graphics"
10
11# Tokenize the input
12inputs = tokenizer(user_profile, return_tensors="pt")
13
14# Generate output
15output = model.generate(inputs["input_ids"])
16
17# Decode and print result
18print(tokenizer.decode(output[0], skip_special_tokens=True))
19
20- PEFT 0.18.1
