Instructions to use kaushik-harsh-99/Math-Instruct-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kaushik-harsh-99/Math-Instruct-v1 with Transformers:

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

pipe = pipeline("text-generation", model="kaushik-harsh-99/Math-Instruct-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kaushik-harsh-99/Math-Instruct-v1")
model = AutoModelForCausalLM.from_pretrained("kaushik-harsh-99/Math-Instruct-v1")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kaushik-harsh-99/Math-Instruct-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kaushik-harsh-99/Math-Instruct-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kaushik-harsh-99/Math-Instruct-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kaushik-harsh-99/Math-Instruct-v1

SGLang

How to use kaushik-harsh-99/Math-Instruct-v1 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 "kaushik-harsh-99/Math-Instruct-v1" \
    --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": "kaushik-harsh-99/Math-Instruct-v1",
		"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 "kaushik-harsh-99/Math-Instruct-v1" \
        --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": "kaushik-harsh-99/Math-Instruct-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kaushik-harsh-99/Math-Instruct-v1 with Docker Model Runner:
```
docker model run hf.co/kaushik-harsh-99/Math-Instruct-v1
```

Math-Instruct-v1 / README.md

kaushik-harsh-99

Update README.md

988f86d verified about 1 month ago

preview code

Raw

History Blame Contribute Delete

1.57 kB

	---
	license: apache-2.0
	language:
	- en
	tags:
	- mathematics
	- math
	- sft
	- instruction-tuning
	- transformers
	pipeline_tag: text-generation
	pretty_name: MathInstruct v1
	library_name: transformers
	datasets:
	- nvidia/OpenMathInstruct-2
	base_model:
	- Qwen/Qwen3-0.6B
	---

	# MathInstruct v1

	MathInstruct v1 is a mathematics-focused instruction-tuned language model created by supervised fine-tuning a pretrained base model on curated mathematics training data.

	This release aims to improve mathematical instruction following, solution generation, and benchmark performance while maintaining the original capabilities of the base model.


	## Results

	Benchmark performance compared with the original base model is shown below.

	![Benchmark Results](./1.png)

	MathInstruct v1 demonstrates improvements across mathematical evaluation tasks and stronger instruction-following behavior.

	## Training

	MathInstruct v1 was trained using supervised fine-tuning (SFT) on the NVIDIA OpenMath dataset.

	The model was trained for 0.1 epoch to adapt the base model toward stronger mathematical instruction following and solution generation while preserving its original capabilities.

	Training setup:

	* Supervised fine-tuning (SFT)
	* Dataset: NVIDIA OpenMath
	* Training duration: 0.1 epoch
	* No manual filtering or removal of noisy samples
	* Original dataset distribution preserved
	* Minimal preprocessing for training compatibility


	## Limitations

	The model may still generate incorrect reasoning or inaccurate answers. Verify outputs before using them in important scenarios.