Instructions to use abhinav241998/qwen3-4b-fitsense-qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abhinav241998/qwen3-4b-fitsense-qlora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

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

Transformers

How to use abhinav241998/qwen3-4b-fitsense-qlora with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="abhinav241998/qwen3-4b-fitsense-qlora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("abhinav241998/qwen3-4b-fitsense-qlora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use abhinav241998/qwen3-4b-fitsense-qlora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abhinav241998/qwen3-4b-fitsense-qlora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abhinav241998/qwen3-4b-fitsense-qlora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/abhinav241998/qwen3-4b-fitsense-qlora

SGLang

How to use abhinav241998/qwen3-4b-fitsense-qlora with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "abhinav241998/qwen3-4b-fitsense-qlora" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abhinav241998/qwen3-4b-fitsense-qlora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "abhinav241998/qwen3-4b-fitsense-qlora" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abhinav241998/qwen3-4b-fitsense-qlora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use abhinav241998/qwen3-4b-fitsense-qlora with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for abhinav241998/qwen3-4b-fitsense-qlora to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for abhinav241998/qwen3-4b-fitsense-qlora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for abhinav241998/qwen3-4b-fitsense-qlora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="abhinav241998/qwen3-4b-fitsense-qlora",
    max_seq_length=2048,
)

Docker Model Runner
How to use abhinav241998/qwen3-4b-fitsense-qlora with Docker Model Runner:
```
docker model run hf.co/abhinav241998/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

Format	Path	Description
LoRA adapter	`/` (repo root)	QLoRA adapter weights, load with PEFT
BF16 merged	`final_merged/bf16/`	Full merged model in bfloat16
AWQ quantized	`final_merged/awq/`	4-bit AWQ quantized merged model

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

Parameter	Value
Learning rate	3.46e-4
LR scheduler	cosine
Warmup ratio	0.05
Batch size	1
Gradient accumulation steps	8 (effective batch 8)
Max grad norm	1.0

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

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