What are the hardware requirements for Mistral 7B Instruct V0.2 Fp16?

Mistral 7B Instruct V0.2 Fp16 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 7B Instruct V0.2 Fp16 and how large is it?

Mistral 7B Instruct V0.2 Fp16 is developed by unrahul, 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 7B Instruct V0.2 Fp16 perform on standard benchmarks?

Mistral 7B Instruct V0.2 Fp16 has the following published scores: MMLU 60.78, LMSYS ELO 1072. Compare against reference models on this page or on the LLM Explorer leaderboards.

Mistral 7B Instruct V0.2 Fp16 by unrahul — VRAM 14.4GB, 32K context

Name: Mistral 7B Instruct V0.2 Fp16
Author: unrahul

Mistral 7B Instruct V0.2 Fp16 is an open-source language model by unrahul. Features: 7b LLM, VRAM: 14.4GB, Context: 32K, Quantized, Instruction-Based, LLM Explorer Score: 0.24, ELO: 1072, Arc: 63.1, HellaSwag: 84.9, MMLU: 60.8, GSM8K: 40.

Autotrain compatible Conversational Endpoints compatible Fp16 Instruct Mistral Pytorch Quantized Region:us Sharded

Model Card on HF 🤗: https://huggingface.co/unrahul/Mistral-7B-Instruct-v0.2-fp16

Mistral 7B Instruct V0.2 Fp16 Benchmarks

LMSys ELO 1072 vs 1272 (so35) -15.7%

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

Mistral 7B Instruct V0.2 Fp16 Parameters and Internals

LLM Name	Mistral 7B Instruct V0.2 Fp16
Repository 🤗	https://huggingface.co/unrahul/Mistral-7B-Instruct-v0.2-fp16
Base Model(s)	jan-hq/Mistral-7B-Instruct-v0.2-SLERP jan-hq/Mistral-7B-Instruct-v0.2-SLERP
Model Size	7b
Required VRAM	14.4 GB
Updated	2025-01-27
Maintainer	unrahul
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
Quantization Type	fp16
Model Architecture	MistralForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.37.0
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	float16

Rank the Mistral 7B Instruct V0.2 Fp16 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 7B Instruct V0.2 Fp16

Best Alternatives	Context / RAM	Downloads	Likes
...aude Sonnet Mistral 7B It V0.3	32K / 14.5 GB	7	2
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	5	0
... 7B Instruct V0.3ContinuedFine	32K / 14.5 GB	8	0
Mistral 7B Instruct V0.3 4bit	32K / 4.1 GB	12492	10
...eMistral 7B Instruct Ead 16bit	32K / 14.5 GB	38	0
NaturalLM 7B Instruct	32K / 14.5 GB	17	10

Note: green Score (e.g. "73.2") means that the model is better than unrahul/Mistral-7B-Instruct-v0.2-fp16.