Text Generation
Transformers
Safetensors
Arabic
English
gemma
text-generation-inference
4-bit precision
bitsandbytes
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("m7alek/MathQA")
model = AutoModelForCausalLM.from_pretrained("m7alek/MathQA")Quick Links
A newer version of this model is available: google/gemma-7b-aps-it
This directory includes a few sample datasets to get you started.
california_housing_data*.csvis California housing data from the 1990 US Census; more information is available at: https://docs.google.com/document/d/e/2PACX-1vRhYtsvc5eOR2FWNCwaBiKL6suIOrxJig8LcSBbmCbyYsayia_DvPOOBlXZ4CAlQ5nlDD8kTaIDRwrN/pubmnist_*.csvis a small sample of the MNIST database, which is described at: http://yann.lecun.com/exdb/mnist/anscombe.jsoncontains a copy of Anscombe's quartet; it was originally described inAnscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American Statistician. 27 (1): 17-21. JSTOR 2682899.
and our copy was prepared by the vega_datasets library.
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Model tree for m7alek/MathQA
Base model
meta-llama/Llama-3.1-70B Finetuned
meta-llama/Llama-3.1-70B-Instruct
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="m7alek/MathQA")