What are the hardware requirements for Sherlock Gemma Finetuned?

Sherlock Gemma Finetuned requires approximately 2.2 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 Sherlock Gemma Finetuned and how large is it?

Sherlock Gemma Finetuned is developed by patilurjit16, a model with 2.5b 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 Sherlock Gemma Finetuned?

Sherlock Gemma Finetuned is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Sherlock Gemma Finetuned by patilurjit16 — VRAM 2.2GB, 8K context

Name: Sherlock Gemma Finetuned
Author: patilurjit16

Sherlock Gemma Finetuned is an open-source language model by patilurjit16. Features: 2.5b LLM, VRAM: 2.2GB, Context: 8K, Fine-Tuned, Merged, LLM Explorer Score: 0.13.

Merged Model Arxiv:1910.09700 4-bit Bitsandbytes Endpoints compatible Finetuned Gemma Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/patilurjit16/sherlock_gemma_finetuned

Sherlock Gemma Finetuned Parameters and Internals

LLM Name	Sherlock Gemma Finetuned
Repository 🤗	https://huggingface.co/patilurjit16/sherlock_gemma_finetuned
Merged Model	Yes
Model Size	2.5b
Required VRAM	2.2 GB
Updated	2026-08-07
Maintainer	patilurjit16
Model Type	gemma
Model Files	2.2 GB
Model Architecture	GemmaForCausalLM
Context Length	8192
Model Max Length	8192
Transformers Version	4.41.2
Vocabulary Size	256000
Torch Data Type	float16

Rank the Sherlock Gemma Finetuned 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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