What are the hardware requirements for V9 Mass Spec Calibration?

V9 Mass Spec Calibration requires approximately 5.2 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 V9 Mass Spec Calibration and how large is it?

V9 Mass Spec Calibration is developed by mohammedismailmi, 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 V9 Mass Spec Calibration?

V9 Mass Spec Calibration is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

V9 Mass Spec Calibration by mohammedismailmi — VRAM 5.2GB, 8K context

Name: V9 Mass Spec Calibration
Author: mohammedismailmi

V9 Mass Spec Calibration is an open-source language model by mohammedismailmi. Features: 2b LLM, VRAM: 5.2GB, Context: 8K, License: gemma, LLM Explorer Score: 0.3.

Base model:finetune:google/gem... Base model:google/gemma-2-2b-i... Conversational Gemma2 Mlx Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/mohammedismailmi/v9-mass-spec-calibration

V9 Mass Spec Calibration Parameters and Internals

LLM Name	V9 Mass Spec Calibration
Repository 🤗	https://huggingface.co/mohammedismailmi/v9-mass-spec-calibration
Base Model(s)	Gemma 2 2B It google/gemma-2-2b-it
Model Size	2b
Required VRAM	5.2 GB
Updated	2026-10-22
Maintainer	mohammedismailmi
Model Type	gemma2
Model Files	5.2 GB
Model Architecture	Gemma2ForCausalLM
License	gemma
Context Length	8192
Model Max Length	8192
Transformers Version	4.42.4
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
Vocabulary Size	256000
Torch Data Type	bfloat16

Rank the V9 Mass Spec Calibration 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 mohammedismailmi/v9-mass-spec-calibration.