Instructions to use Harsha901/Qwen3-4B-Inst-Math-Reasoning-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Harsha901/Qwen3-4B-Inst-Math-Reasoning-SFT with Transformers:

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

pipe = pipeline("text-generation", model="Harsha901/Qwen3-4B-Inst-Math-Reasoning-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Harsha901/Qwen3-4B-Inst-Math-Reasoning-SFT")
model = AutoModelForCausalLM.from_pretrained("Harsha901/Qwen3-4B-Inst-Math-Reasoning-SFT", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Harsha901/Qwen3-4B-Inst-Math-Reasoning-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Harsha901/Qwen3-4B-Inst-Math-Reasoning-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Harsha901/Qwen3-4B-Inst-Math-Reasoning-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Harsha901/Qwen3-4B-Inst-Math-Reasoning-SFT

SGLang

How to use Harsha901/Qwen3-4B-Inst-Math-Reasoning-SFT 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 "Harsha901/Qwen3-4B-Inst-Math-Reasoning-SFT" \
    --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": "Harsha901/Qwen3-4B-Inst-Math-Reasoning-SFT",
		"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 "Harsha901/Qwen3-4B-Inst-Math-Reasoning-SFT" \
        --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": "Harsha901/Qwen3-4B-Inst-Math-Reasoning-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Harsha901/Qwen3-4B-Inst-Math-Reasoning-SFT 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 Harsha901/Qwen3-4B-Inst-Math-Reasoning-SFT 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 Harsha901/Qwen3-4B-Inst-Math-Reasoning-SFT to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Harsha901/Qwen3-4B-Inst-Math-Reasoning-SFT to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Harsha901/Qwen3-4B-Inst-Math-Reasoning-SFT",
    max_seq_length=2048,
)

Docker Model Runner
How to use Harsha901/Qwen3-4B-Inst-Math-Reasoning-SFT with Docker Model Runner:
```
docker model run hf.co/Harsha901/Qwen3-4B-Inst-Math-Reasoning-SFT
```

Uploaded Model

Developed by: Harsha901
License: apache-2.0
Finetuned from model: unsloth/Qwen3-4B-Instruct-2507

This Qwen3 model was trained ~2× faster using Unsloth and Hugging Face’s TRL library.

📌 Model Overview

Qwen3-4B-Inst-Math-Reasoning-SFT is a supervised fine-tuned (SFT) variant of Qwen3-4B-Instruct, optimized for mathematical reasoning and step-by-step problem solving.

The model is trained to follow instructions precisely while producing clear, logically structured reasoning chains, making it suitable for:

Math problem solving
Educational assistants
Reasoning benchmarks
Downstream alignment (DPO / RLHF)

🧠 Key Capabilities

Multi-step mathematical reasoning
Algebra, arithmetic, and word problems
Chain-of-thought style explanations
Improved instruction adherence
More stable reasoning compared to the base model

🏗️ Model Architecture

Architecture: Decoder-only Transformer (Causal LM)
Parameters: ~4B
Base Model: Qwen3-4B-Instruct (Unsloth optimized)
Tokenization: Qwen tokenizer
Context Length: Same as base model

📚 Training Data

The model was fine-tuned on a curated dataset consisting of:

Instruction-style math prompts
Step-by-step mathematical solutions
Reasoning-focused explanations

Data was filtered to emphasize:

Logical consistency
Clear intermediate steps
Reduced ambiguity in solutions

While care was taken to ensure quality, the dataset may still contain noise or biases present in public mathematical corpora.

⚙️ Training Details

Fine-tuning Method: Supervised Fine-Tuning (SFT)
Frameworks: Hugging Face Transformers + TRL
Acceleration: Unsloth (memory-efficient & faster training)
Precision: FP16 / BF16 (hardware dependent)
Optimizer: AdamW
Loss Function: Cross-entropy
Batching: Gradient accumulation for memory efficiency

🚀 Usage

Load the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Harsha901/Qwen3-4B-Inst-Math-Reasoning-SFT"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto"
)

Example Inference

prompt = "Solve step by step: If 5x − 10 = 15, find x."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.2,
    do_sample=False
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📊 Evaluation

The model was evaluated qualitatively on:

Math word problems
Algebraic equations
Multi-step reasoning tasks

Observed improvements vs base model:

Better structured reasoning
More consistent intermediate steps
Fewer incomplete solutions

Formal benchmark results (e.g., GSM8K, MATH) are planned for future updates.

⚠️ Limitations

Not guaranteed to be mathematically correct in all cases
Can be verbose due to reasoning-style outputs
Not optimized for creative or non-technical writing
Performance may degrade on extremely long or ambiguous prompts

🔐 Ethical & Responsible Use

Intended for research and educational purposes
Outputs should be verified for correctness in critical applications
Not suitable for high-stakes decision-making without human oversight

📜 License

Released under the Apache 2.0 License, consistent with the base Qwen3 model.

🙌 Acknowledgements

Qwen Team for the base Qwen3 architecture
Unsloth for efficient fine-tuning optimizations
Hugging Face for Transformers and TRL

✉️ Author

Harsha Vardhan Mannem AI / ML Engineer Hugging Face & GitHub: Harsha901

🔮 Future Work

Preference tuning with DPO
Quantized inference (4-bit / 8-bit)
Benchmark-based evaluation
Deployment-optimized variants

Downloads last month: 6

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for Harsha901/Qwen3-4B-Inst-Math-Reasoning-SFT

Base model

Qwen/Qwen3-4B-Instruct-2507

Finetuned

unsloth/Qwen3-4B-Instruct-2507

Finetuned

(424)

this model

Collection including Harsha901/Qwen3-4B-Inst-Math-Reasoning-SFT

Math & Reasoning LLMs

Collection

Math & Reasoning LLMs by Harsha901 • 6 items • Updated Jan 3