What are the hardware requirements for Gemma Code Instruct Finetune Python?

Gemma Code Instruct Finetune Python requires approximately 5.1 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma Code Instruct Finetune Python and how large is it?

Gemma Code Instruct Finetune Python is developed by wambugu1738, a model with 2b 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 Gemma Code Instruct Finetune Python?

Gemma Code Instruct Finetune Python is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Gemma Code Instruct Finetune Python by wambugu1738 — VRAM 5.1GB, 8K context

Name: Gemma Code Instruct Finetune Python
Author: wambugu1738

Gemma Code Instruct Finetune Python is an open-source language model by wambugu1738. Features: 2b LLM, VRAM: 5.1GB, Context: 8K, Fine-Tuned, Instruction-Based, LLM Explorer Score: 0.11.

Arxiv:1910.09700 Autotrain compatible Conversational Endpoints compatible Finetuned Gemma Instruct Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/wambugu1738/gemma-Code-Instruct-Finetune-Python

Gemma Code Instruct Finetune Python Parameters and Internals

LLM Name	Gemma Code Instruct Finetune Python
Repository 🤗	https://huggingface.co/wambugu1738/gemma-Code-Instruct-Finetune-Python
Model Size	2b
Required VRAM	5.1 GB
Updated	2024-11-13
Maintainer	wambugu1738
Model Type	gemma
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-2 0.1 GB: 2-of-2
Model Architecture	GemmaForCausalLM
Context Length	8192
Model Max Length	8192
Transformers Version	4.38.0
Tokenizer Class	GemmaTokenizer
Padding Token	<eos>
Vocabulary Size	256000
Torch Data Type	float16

Rank the Gemma Code Instruct Finetune Python capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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

Quantized Models of the Gemma Code Instruct Finetune Python

Model	Likes	Downloads	VRAM
...ma Code Instruct Finetune Test	0	45	5 GB

Best Alternatives to Gemma Code Instruct Finetune Python

Best Alternatives	Context / RAM	Downloads	Likes
Gita SastraGPT V1	8K / 5.1 GB	4	1
Pandas Tutor Gemma 2B	8K / 5.1 GB	6	1
... 2B Finetuned Sft Navarasa 2.0	8K / 10 GB	536	27
...ma Code Instruct Finetune Test	8K / 5.1 GB	30	0
Gemma 2B Sft Preview	8K / 5 GB	13	0
Leniachat Gemma 2B V0	8K / 15.1 GB	57	14
...ma Instruct Finetune On Alpaca	8K / 5.1 GB	5	0
...ma Code Instruct Finetune Test	8K / 5.1 GB	10	0
Gemma 2B Persona Instruct	8K / 5.1 GB	5	0
...ma Code Instruct Finetune Test	8K / 5.1 GB	6	0

Note: green Score (e.g. "73.2") means that the model is better than wambugu1738/gemma-Code-Instruct-Finetune-Python.