qwen3-4b-fitsense-qlora

FitSenseAI — Qwen3-4B QLoRA Adapter

A LoRA adapter fine-tuned on top of unsloth/Qwen3-4B for AI-powered fitness coaching. The model is a tool-calling agent that generates personalized workout plans, logs workouts, tracks health metrics, analyzes progress, and answers fitness coaching questions.

Available Formats

Model Details

• Base model: unsloth/Qwen3-4B
• Fine-tuning method: QLoRA (4-bit base + LoRA adapters)
• Task: Supervised fine-tuning (SFT) on synthetic fitness coaching conversations
• LoRA rank: 8 | LoRA alpha: 16 | Dropout: 0
• Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• Training steps: 270 over 3 epochs
• Training time: 42m 36s
• Max sequence length: 16,500 tokens
• Precision: bfloat16

Training Hyperparameters

How to Use

LoRA Adapter

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-4B")
tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen3-4B")
model = PeftModel.from_pretrained(base_model, "abhinav241998/qwen3-4b-fitsense-qlora")

BF16 Merged Model

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "abhinav241998/qwen3-4b-fitsense-qlora",
    subfolder="final_merged/bf16",
    torch_dtype="bfloat16",
)
tokenizer = AutoTokenizer.from_pretrained(
    "abhinav241998/qwen3-4b-fitsense-qlora",
    subfolder="final_merged/bf16",
)

AWQ Quantized Model

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "abhinav241998/qwen3-4b-fitsense-qlora",
    subfolder="final_merged/awq",
)
tokenizer = AutoTokenizer.from_pretrained(
    "abhinav241998/qwen3-4b-fitsense-qlora",
    subfolder="final_merged/awq",
)

Framework Versions

• PEFT 0.18.1
• Transformers 4.43+
• TRL 0.9+
• Unsloth