What are the hardware requirements for Gemma 2 2B It CoT It SFT?

Gemma 2 2B It CoT It SFT requires approximately 6.4 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 2 2B It CoT It SFT and how large is it?

Gemma 2 2B It CoT It SFT is developed by RyanYr, 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 2 2B It CoT It SFT?

Gemma 2 2B It CoT It SFT is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Gemma 2 2B It CoT It SFT by RyanYr — VRAM 6.4GB, 8K context

Name: Gemma 2 2B It CoT It SFT
Author: RyanYr

Gemma 2 2B It CoT It SFT is an open-source language model by RyanYr. Features: 2b LLM, VRAM: 6.4GB, Context: 8K, License: gemma, LLM Explorer Score: 0.14.

Autotrain compatible Base model:finetune:google/gem... Base model:google/gemma-2-2b-i... Conversational Endpoints compatible Gemma2 Generated from trainer Region:us Safetensors Sft Sharded Tensorflow Trl

Model Card on HF 🤗: https://huggingface.co/RyanYr/gemma-2-2b-it_CoT-it_SFT

Gemma 2 2B It CoT It SFT Parameters and Internals

LLM Name	Gemma 2 2B It CoT It SFT
Repository 🤗	https://huggingface.co/RyanYr/gemma-2-2b-it_CoT-it_SFT
Base Model(s)	Gemma 2 2B It google/gemma-2-2b-it
Model Size	2b
Required VRAM	6.4 GB
Updated	2024-10-10
Maintainer	RyanYr
Model Type	gemma2
Model Files	5.0 GB: 1-of-2 1.4 GB: 2-of-2 0.0 GB
Model Architecture	Gemma2ForCausalLM
License	gemma
Context Length	8192
Model Max Length	8192
Transformers Version	4.44.2
Tokenizer Class	GemmaTokenizer
Padding Token	[PAD]
Vocabulary Size	256001
Torch Data Type	bfloat16

Rank the Gemma 2 2B It CoT It SFT 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

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Note: green Score (e.g. "73.2") means that the model is better than RyanYr/gemma-2-2b-it_CoT-it_SFT.