Instructions to use chowdhuryshaif/algebra-t5-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chowdhuryshaif/algebra-t5-model with Transformers:

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

pipe = pipeline("text-generation", model="chowdhuryshaif/algebra-t5-model")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("chowdhuryshaif/algebra-t5-model")
model = AutoModelForSeq2SeqLM.from_pretrained("chowdhuryshaif/algebra-t5-model")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use chowdhuryshaif/algebra-t5-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chowdhuryshaif/algebra-t5-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chowdhuryshaif/algebra-t5-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/chowdhuryshaif/algebra-t5-model

SGLang

How to use chowdhuryshaif/algebra-t5-model 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 "chowdhuryshaif/algebra-t5-model" \
    --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": "chowdhuryshaif/algebra-t5-model",
		"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 "chowdhuryshaif/algebra-t5-model" \
        --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": "chowdhuryshaif/algebra-t5-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use chowdhuryshaif/algebra-t5-model with Docker Model Runner:
```
docker model run hf.co/chowdhuryshaif/algebra-t5-model
```

Algebra T5 Model

Model Description

This model is a fine-tuned version of FLAN-T5 designed to generate explanations and responses related to school-level algebra concepts.

The model was trained on instructional algebra content and exercises to help generate explanations, examples, and basic reasoning about algebra topics commonly encountered in secondary education.

The goal of the model is to support experimentation with AI-assisted explanations for introductory algebra topics.

Model Details

Developed by: Shaif Chowdhury
Model type: Sequence-to-sequence text generation
Base model: google/flan-t5-base
Language: English
Domain: School algebra / mathematics education
License: Apache 2.0 (same as base model)

Intended Use

Direct Use

This model can be used for:

Explaining algebra concepts
Generating short algebra explanations
Producing educational responses related to school algebra
Experimentation with AI-assisted math tutoring

Example tasks:

Explain algebra
Describe algebra concepts
Generate simple explanations of algebra topics

Example Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("USERNAME/algebra-t5-model")
model = AutoModelForSeq2SeqLM.from_pretrained("USERNAME/algebra-t5-model")

prompt = "explain algebra"

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)

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

Downloads last month: 3

Safetensors

Model size

0.2B params

Tensor type

F32