What are the hardware requirements for Python Code 33B?

Python Code 33B requires approximately 65.3 GB of VRAM and supports a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Python Code 33B and how large is it?

Python Code 33B is developed by ajibawa-2023, a model with 33b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Python Code 33B perform on standard benchmarks?

Python Code 33B has the following published scores: MMLU 54.22. Compare against reference models on this page or on the LLM Explorer leaderboards.

Python Code 33B by ajibawa-2023 — VRAM 65.3GB, 2K context

Name: Python Code 33B
Author: ajibawa-2023

Python Code 33B is an open-source language model by ajibawa-2023. Features: 33b LLM, VRAM: 65.3GB, Context: 2K, License: cc-by-nc-nd-4.0, LLM Explorer Score: 0.14, Arc: 56.3, HellaSwag: 81, MMLU: 54.2, GSM8K: 19.2.

Code Dataset:ajibawa-2023/python-co... En Endpoints compatible Llama Model-index Pytorch Region:us Sharded

Model Card on HF 🤗: https://huggingface.co/ajibawa-2023/Python-Code-33B

Python Code 33B Benchmarks

ARC: 56.31 vs 96.7 (so35)^-41.8%

HellaSwag: 81.01 vs 95.3 (gpt4)^-15%

MMLU: 54.22 vs 88.3 (so35)^-38.6%

GSM8K: 19.18 vs 96.4 (so35)^-80.1%

LLME Score: 0.13873

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

Python Code 33B (ajibawa-2023/Python-Code-33B)

🌟 Advertise your project 🚀

Python Code 33B Parameters and Internals

Model Type

text-generation

Use Cases

Areas:

Code generation

Applications:

Research, Software development

Primary Use Cases:

Generating Python code with explanations

Limitations:

Possible mistakes in code generation

Considerations:

Model provides detailed explanations with code.

Additional Notes

Model also provides detailed explanations along with code generation.

Training Details

Data Sources:

Generated using GPT-3.5, GPT-4, Vicuna/ShareGPT format

Data Volume:

23000+ set of codes

Methodology:

Fine-tuning

Training Time:

42 hours on Azure 4 x A100 80GB for 3 epochs

Hardware Used:

Azure 4 x A100 80GB

Model Architecture:

Trained on Llama-1 by Meta

Input Output

Input Format:

Vicuna/ShareGPT format

Accepted Modalities:

text

Output Format:

Python code with explanations

Performance Tips:

Ensure input format is consistent with trained data format.

LLM Name	Python Code 33B
Repository 🤗	https://huggingface.co/ajibawa-2023/Python-Code-33B
Model Size	33b
Required VRAM	65.3 GB
Updated	2026-05-13
Maintainer	ajibawa-2023
Model Type	llama
Model Files	8.5 GB: 1-of-8 8.6 GB: 2-of-8 8.6 GB: 3-of-8 8.6 GB: 4-of-8 8.6 GB: 5-of-8 8.6 GB: 6-of-8 8.6 GB: 7-of-8 5.2 GB: 8-of-8
Supported Languages	en
Model Architecture	LlamaForCausalLM
License	cc-by-nc-nd-4.0
Context Length	2048
Model Max Length	2048
Transformers Version	4.28.1
Tokenizer Class	LlamaTokenizer
Beginning of Sentence Token	<s>
End of Sentence Token	</s>
Unk Token	<unk>
Vocabulary Size	32000
Torch Data Type	bfloat16

Quantized Models of the Python Code 33B

Model	Likes	Downloads	VRAM
Python Code 33B GGUF	3	1328	13 GB
Python Code 33B GPTQ	4	10	16 GB
Python Code 33B AWQ	1	17	17 GB

Best Alternatives to Python Code 33B

Best Alternatives	Context / RAM	Downloads	Likes
...angled Llama 33M 32K Base V0.1	32K / 0.1 GB	22	1
ReflectionCoder DS 33B	16K / 67 GB	8190	4
Deepseek Wizard 33B Slerp	16K / 35.3 GB	10	0
ValidateAI 33B Slerp	16K / 35.4 GB	6	0
WhiteRabbitNeo 33B V1	16K / 67 GB	1089	91
ValidateAI 3 33B Ties	16K / 66.5 GB	8	0
ValidateAI 2 33B AT	16K / 66.5 GB	6	0
Chronos Divergence 33B	16K / 65 GB	19	30
Deepseek Coder 33B Instruct	16K / 66.5 GB	4418	566
Everyone Coder 33B Base	16K / 66.5 GB	98	21

Note: green Score (e.g. "73.2") means that the model is better than ajibawa-2023/Python-Code-33B.

Rank the Python Code 33B Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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

What open-source LLMs or SLMs are you in search of? 53999 in total.

Email us: info@extractum.io. Our Privacy Policy | Terms and Conditions | Suggest an improvement.

Our Social Media →

Original data from HuggingFace, OpenCompass and various public git repos.

Check out Ag3ntum — our secure, self-hosted AI agent for server management.

Release v20260328a

Support LLM Explorer

Python Code 33B by ajibawa-2023