Instructions to use harsh762011/numinao14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use harsh762011/numinao14 with Transformers:

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

pipe = pipeline("text-generation", model="harsh762011/numinao14")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("harsh762011/numinao14")
model = AutoModelForCausalLM.from_pretrained("harsh762011/numinao14")
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

vLLM

How to use harsh762011/numinao14 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "harsh762011/numinao14"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "harsh762011/numinao14",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/harsh762011/numinao14

SGLang

How to use harsh762011/numinao14 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 "harsh762011/numinao14" \
    --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": "harsh762011/numinao14",
		"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 "harsh762011/numinao14" \
        --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": "harsh762011/numinao14",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use harsh762011/numinao14 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 harsh762011/numinao14 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 harsh762011/numinao14 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for harsh762011/numinao14 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="harsh762011/numinao14",
    max_seq_length=2048,
)

Docker Model Runner
How to use harsh762011/numinao14 with Docker Model Runner:
```
docker model run hf.co/harsh762011/numinao14
```

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Uploaded finetuned model

Developed by: Harsh Srivastava
License: cc-by-nc-3.0
Finetuned from model : unsloth/phi-4-mini-reasoning This phi3 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Phi-4 Mini Reasoning – JEE Mathematics Finetuned Model

Developer

Harsh Srivastava

Base Model

unsloth/phi-4-mini-reasoning

Description

This model is a finetuned version of Phi-4 Mini Reasoning designed for solving JEE-level mathematics problems.

The model is optimized for step-by-step mathematical reasoning and symbolic problem solving.

Training Dataset

Total samples used: 356,532 not that much but above 200k samples trained we are still training it better on various datasets for jee by the help of the keyword filters

Sources: "AI-MO/NuminaMath-CoT 293k samples on one epoch

AI-MO/NuminaMath-TIR — 68,850 same with this dataset
MetaMathQA —70000 on one epoch rest all are on one epoch
TIGER-Lab MathInstruct — 125,220
PhysicsWallahAI JEE Main 2025 (Jan) — 182
PhysicsWallahAI JEE Main 2025 (Apr) — 169
MMLU High School Mathematics — 78
MMLU College Mathematics — 50
MMLU Abstract Algebra — 25

Training Details

Base model: Phi-4 Mini Reasoning
Framework: Unsloth + HuggingFace TRL
Training method: LoRA finetuning
Sequence length: 2048
Optimizer: AdamW 8bit

Purpose

The model is designed for:

JEE mathematics reasoning
Step-by-step mathematical explanations
Competitive exam problem solving

Downloads last month: 6

Safetensors

Model size

4B params

Tensor type

F16