Instructions to use Nasim435/Qwen-3B-Automotive-4000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nasim435/Qwen-3B-Automotive-4000 with Transformers:

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

pipe = pipeline("text-generation", model="Nasim435/Qwen-3B-Automotive-4000")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nasim435/Qwen-3B-Automotive-4000")
model = AutoModelForCausalLM.from_pretrained("Nasim435/Qwen-3B-Automotive-4000", device_map="auto")

PEFT
How to use Nasim435/Qwen-3B-Automotive-4000 with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Nasim435/Qwen-3B-Automotive-4000 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nasim435/Qwen-3B-Automotive-4000"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nasim435/Qwen-3B-Automotive-4000",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Nasim435/Qwen-3B-Automotive-4000

SGLang

How to use Nasim435/Qwen-3B-Automotive-4000 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 "Nasim435/Qwen-3B-Automotive-4000" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nasim435/Qwen-3B-Automotive-4000",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Nasim435/Qwen-3B-Automotive-4000" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nasim435/Qwen-3B-Automotive-4000",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Nasim435/Qwen-3B-Automotive-4000 with Docker Model Runner:
```
docker model run hf.co/Nasim435/Qwen-3B-Automotive-4000
```

QWEN-3B

MODEL CARD / AUTOMOTIVE DOMAIN ■ ONLINE

QWEN-3B
AUTOMOTIVE

BASE → Qwen/Qwen2.5-3B-Instruct · METHOD → QLoRA / 4-bit NF4 · SAMPLES → 4,000

QLoRA Automotive PEFT SFT 4-bit NF4 bfloat16 TRL BitsAndBytes Apache 2.0

OVERVIEW

Domain-adapted variant of Qwen2.5-3B-Instruct, fine-tuned on automotive instruction-following data using QLoRA (4-bit quantized LoRA fine-tuning). Created as an experimental learning and research project focused on modern parameter-efficient fine-tuning workflows in 2026-style LLM engineering.

Specialized toward automotive-related question answering and technical explanations. Trained on a curated subset of 4,000 samples from the BAAI automotive industry instruction dataset.

Model Identity

BASEQwen/Qwen2.5-3B-Instruct

DEVELOPED BYAlibaba Cloud / Qwen Team

FINE-TUNED BYNasim Raj Laskar

LANGUAGEEnglish

LICENSEApache 2.0

Capabilities

► Automotive Q&A

► Diagnostic Explanations

► Vehicle Systems Knowledge

► Maintenance & Repair Instructions

► PEFT / QLoRA Experimentation

TRAINING DATA

Fine-tuned on the English subset of BAAI/IndustryInstruction_Automobiles. A custom subset of approximately 4,000 automotive instruction samples was selected for training.

Dataset Processing Pipeline

FORMATQwen chat-template conversations

STRUCTUREsystem → user → assistant

SYSTEM PROMPT"You are an automotive expert assistant."

SAMPLES~4,000 curated instruction pairs

◆ DATASET CONTENTS

The dataset consists primarily of automotive technical Q&A, diagnostic explanations, vehicle systems knowledge, and maintenance and repair related instructions.

TRAINING CONFIGURATION

Method

QLoRA

Quantization

4-bit NF4

Precision

bfloat16

Optimizer

AdamW 8bit

Max Seq Len

512

Packing

Disabled

Infrastructure

PLATFORMAWS SageMaker

INSTANCEml.g5.xlarge

GPUNVIDIA A10G (24GB VRAM)

Hyperparameters

EPOCHS1

LEARNING RATE2e-5

BATCH SIZE4 (grad accum × 2)

► LoRA Adapter Parameters

Rank (r)

Alpha (α)

Dropout

0.05

Trainable Params

~30M

Total Params

~3.1B

Libraries

Transformers · TRL · PEFT · BitsAndBytes

Accelerate · Datasets

Fine-Tuning Stack

QLoRA · PEFT (LoRA adapters)

SFT · 4-bit NF4 quantization

INTENDED USE

► Automotive Question Answering

► Educational Demonstrations

► PEFT / QLoRA Experimentation

► Automotive Technical Assistance

► LLM Fine-Tuning Research

► Domain Adaptation Studies

EXAMPLE USAGE

Python

# Load model from Hugging Face Hub
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "Nasim435/Qwen-3B-Automotive-4000"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model     = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto"
)
prompt   = "Explain symptoms of a failing alternator."
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
inputs  = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=150,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

LIMITATIONS

⚠ WARNING

Experimental fine-tuned model — not intended for production safety systems
May hallucinate or generate inaccurate automotive advice
Not suitable for safety-critical or professional mechanical decision-making
Trained on a relatively small subset (~4k samples); generalization may be limited

ACKNOWLEDGEMENTS

Qwen TeamAlibaba Cloud

BAAIDataset Contributors

Hugging FaceEcosystem

TRL / PEFTContributors

BitsAndBytesQuantization

AccelerateFramework

DatasetsHuggingFace

Open-SourceLLM Community

Downloads last month: 22

Safetensors

Model size

3B params

Tensor type

F32

F16

Model tree for Nasim435/Qwen-3B-Automotive-4000

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Quantized

(305)

this model

Nasim435
/

Qwen-3B-Automotive-4000

Model tree for Nasim435/Qwen-3B-Automotive-4000

Dataset used to train Nasim435/Qwen-3B-Automotive-4000