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

Libraries

How to use harsh762011/numinao14-new with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("harsh762011/numinao14-new")
model = AutoModelForCausalLM.from_pretrained("harsh762011/numinao14-new")
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-new with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "harsh762011/numinao14-new"
# 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-new",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/harsh762011/numinao14-new

SGLang

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

Unsloth Studio new

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

Load model with FastModel

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

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

Phi-4 Mini Reasoning – JEE Mathematics Finetuned Model

A new version of the model present at harsh762011/numiano14.

Uploaded Finetuned Model

Developed by: Harsh Srivastava
License: cc-by-nc-3.0
Finetuned from model: unsloth/phi-4-mini-reasoning

This Phi-4 model was trained faster using Unsloth and Hugging Face TRL.

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
Symbolic problem solving
Competitive exam-style question solving

Training Dataset

Total samples used: 500k+ filtered mathematics and reasoning samples.

The training pipeline focuses on JEE-level mathematical difficulty using keyword-based dataset filtering.

Sources

AI-MO/NuminaMath-CoT — 293k samples (2 epochs)
AI-MO/NuminaMath-TIR — 68,850 samples
MetaMathQA — 70k samples
TIGER-Lab MathInstruct — 125,220 samples
PhysicsWallahAI JEE Main 2025 (Jan) — 182 samples
PhysicsWallahAI JEE Main 2025 (Apr) — 169 samples
MMLU High School Mathematics — 78 samples
MMLU College Mathematics — 50 samples
MMLU Abstract Algebra — 25 samples

Training Details

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

Intended Purpose

The model is designed for:

JEE mathematics reasoning
Step-by-step mathematical explanations
Competitive exam problem solving
Mathematical chain-of-thought reasoning

Limitations

The model may still generate incorrect mathematical reasoning.
Outputs should be verified for high-stakes usage.
The model is still under active improvement and continued training.

Downloads last month: 99

Safetensors

Model size

4B params

Tensor type

F16