What are the hardware requirements for Mistroll 7B V2.3 NoTsOsm4rt 16bit?

Mistroll 7B V2.3 NoTsOsm4rt 16bit requires approximately 14.4 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Mistroll 7B V2.3 NoTsOsm4rt 16bit and how large is it?

Mistroll 7B V2.3 NoTsOsm4rt 16bit is developed by BarraHome, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Mistroll 7B V2.3 NoTsOsm4rt 16bit perform on standard benchmarks?

Mistroll 7B V2.3 NoTsOsm4rt 16bit has the following published scores: MMLU 65.12. Compare against reference models on this page or on the LLM Explorer leaderboards.

Mistroll 7B V2.3 NoTsOsm4rt 16bit by BarraHome — VRAM 14.4GB, 32K context

Name: Mistroll 7B V2.3 NoTsOsm4rt 16bit
Author: BarraHome

Mistroll 7B V2.3 NoTsOsm4rt 16bit is an open-source language model by BarraHome. Features: 7b LLM, VRAM: 14.4GB, Context: 32K, License: apache-2.0, Quantized, LLM Explorer Score: 0.13, Arc: 67.1, HellaSwag: 86.2, MMLU: 65.1, GSM8K: 66.6.

16bit Autotrain compatible Base model:barrahome/mistroll-... Base model:finetune:barrahome/... Conversational En Endpoints compatible Mistral Quantized Region:us Safetensors Sft Sharded Tensorflow Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/BarraHome/Mistroll-7B-v2.3-NoTsOsm4rt-16bit

Mistroll 7B V2.3 NoTsOsm4rt 16bit Parameters and Internals

Model Type

text-generation-inference, transformers, unsloth, mistral, trl, sft

Training Details

Methodology:

Trained 2x faster with Unsloth and Huggingface's TRL library.

LLM Name	Mistroll 7B V2.3 NoTsOsm4rt 16bit
Repository 🤗	https://huggingface.co/BarraHome/Mistroll-7B-v2.3-NoTsOsm4rt-16bit
Base Model(s)	BarraHome/Mistroll-7B-v2.2 BarraHome/Mistroll-7B-v2.2
Model Size	7b
Required VRAM	14.4 GB
Updated	2024-11-28
Maintainer	BarraHome
Model Type	mistral
Model Files	4.9 GB: 1-of-3 5.0 GB: 2-of-3 4.5 GB: 3-of-3
Supported Languages	en
Quantization Type	16bit
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.40.2
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the Mistroll 7B V2.3 NoTsOsm4rt 16bit 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 BarraHome/Mistroll-7B-v2.3-NoTsOsm4rt-16bit.