What are the hardware requirements for Mistral Rank16 Sft?

Mistral Rank16 Sft 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 Mistral Rank16 Sft and how large is it?

Mistral Rank16 Sft is developed by chatty123, 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 Mistral Rank16 Sft perform on standard benchmarks?

Mistral Rank16 Sft has the following published scores: MMLU 60.43. Compare against reference models on this page or on the LLM Explorer leaderboards.

Mistral Rank16 Sft by chatty123 — VRAM 14.4GB, 32K context

Name: Mistral Rank16 Sft
Author: chatty123

Mistral Rank16 Sft is an open-source language model by chatty123. Features: 7b LLM, VRAM: 14.4GB, Context: 32K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.14, Arc: 55, HellaSwag: 81.2, MMLU: 60.4, GSM8K: 35.2.

4bit Base model:finetune:unsloth/mi... Base model:unsloth/mistral-7b-... Conversational En Endpoints compatible Instruct Mistral Quantized Region:us Safetensors Sft Sharded Tensorflow Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/chatty123/mistral_rank16_sft

Mistral Rank16 Sft Parameters and Internals

Model Type

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

LLM Name	Mistral Rank16 Sft
Repository 🤗	https://huggingface.co/chatty123/mistral_rank16_sft
Base Model(s)	unsloth/mistral-7b-instruct-v0.2-bnb-4bit unsloth/mistral-7b-instruct-v0.2-bnb-4bit
Model Size	7b
Required VRAM	14.4 GB
Updated	2026-08-08
Maintainer	chatty123
Model Type	mistral
Instruction-Based	Yes
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	4bit
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.39.2
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the Mistral Rank16 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

Best Alternatives to Mistral Rank16 Sft

Best Alternatives	Context / RAM	Downloads	Likes
...aude Sonnet Mistral 7B It V0.3	32K / 14.5 GB	7	2
Mistral 7B Instruct V0.2 Fp16	32K / 14.4 GB	26	0
Mistral 7B Instruct V0.2 4bit	32K / 4.3 GB	79	1
...tral 7B Instruct V0.3 Bnb 4bit	32K / 4.1 GB	34633	42
Mistral 7B Instruct V0.2 8bit	32K / 7.6 GB	5	1
...tral 7B Instruct V0.2 Bnb 4bit	32K / 4.1 GB	7160	35
Mixtral V0.3 Full 16bit	32K / 14.5 GB	8	0
... 7B Instruct V0.3ContinuedFine	32K / 14.5 GB	14	0
Mistral 7B Instruct V0.3 4bit	32K / 4.1 GB	12492	10
...eMistral 7B Instruct Ead 16bit	32K / 14.5 GB	38	0

Note: green Score (e.g. "73.2") means that the model is better than chatty123/mistral_rank16_sft.