What are the hardware requirements for OpenMath CodeLlama 13B Python Hf AWQ?

OpenMath CodeLlama 13B Python Hf AWQ requires approximately 7.5 GB of VRAM and supports a context window of 16K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed OpenMath CodeLlama 13B Python Hf AWQ and how large is it?

OpenMath CodeLlama 13B Python Hf AWQ is developed by eabdullin, a model with 13b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate OpenMath CodeLlama 13B Python Hf AWQ?

OpenMath CodeLlama 13B Python Hf AWQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

OpenMath CodeLlama 13B Python Hf AWQ by eabdullin — VRAM 7.5GB, 16K context

Name: OpenMath CodeLlama 13B Python Hf AWQ
Author: eabdullin

OpenMath CodeLlama 13B Python Hf AWQ is an open-source language model by eabdullin. Features: 13b LLM, VRAM: 7.5GB, Context: 16K, Quantized, Code Generating, LLM Explorer Score: 0.12.

4-bit Awq Codegen Endpoints compatible Llama Pytorch Quantized Region:us Sharded

Model Card on HF 🤗: https://huggingface.co/eabdullin/OpenMath-CodeLlama-13b-Python-hf-AWQ

OpenMath CodeLlama 13B Python Hf AWQ Parameters and Internals

LLM Name	OpenMath CodeLlama 13B Python Hf AWQ
Repository 🤗	https://huggingface.co/eabdullin/OpenMath-CodeLlama-13b-Python-hf-AWQ
Base Model(s)	...enMath CodeLlama 13B Python Hf nvidia/OpenMath-CodeLlama-13b-Python-hf
Model Size	13b
Required VRAM	7.5 GB
Updated	2026-07-16
Maintainer	eabdullin
Model Type	llama
Model Files	2.0 GB: 1-of-4 2.0 GB: 2-of-4 2.0 GB: 3-of-4 1.5 GB: 4-of-4
AWQ Quantization	Yes
Quantization Type	awq
Generates Code	Yes
Model Architecture	LlamaForCausalLM
Context Length	16384
Model Max Length	16384
Transformers Version	4.41.0
Tokenizer Class	CodeLlamaTokenizer
Vocabulary Size	32000
Torch Data Type	float16

Rank the OpenMath CodeLlama 13B Python Hf AWQ capabilities

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Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

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Note: green Score (e.g. "73.2") means that the model is better than eabdullin/OpenMath-CodeLlama-13b-Python-hf-AWQ.