What are the hardware requirements for Eclipse Mistral 4x7b?

Eclipse Mistral 4x7b requires approximately 48.5 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 Eclipse Mistral 4x7b and how large is it?

Eclipse Mistral 4x7b is developed by NotAiLOL, a model with 24.2b 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 Eclipse Mistral 4x7b?

Eclipse Mistral 4x7b is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Eclipse Mistral 4x7b by NotAiLOL — VRAM 48.5GB, 32K context

Name: Eclipse Mistral 4x7b
Author: NotAiLOL

Eclipse Mistral 4x7b is an open-source language model by NotAiLOL. Features: 24.2b LLM, VRAM: 48.5GB, Context: 32K, License: apache-2.0, MoE, Instruction-Based, LLM Explorer Score: 0.12.

Autotrain compatible Base model:maziyarpanahi/calme... Base model:merge:maziyarpanahi... Base model:merge:meta-math/met... Base model:merge:nousresearch/... Base model:merge:sanjiwatsuki/... Base model:meta-math/metamath-... Base model:nousresearch/hermes... Base model:nousresearch/hermes... Base model:sanjiwatsuki/kunoic... Conversational Endpoints compatible Instruct Maziyarpanahi/calme-7b-instruc... Merge Mergekit Meta-math/metamath-mistral-7b Mixtral Moe Nousresearch/hermes-2-pro-mist... Region:us Safetensors Sanjiwatsuki/kunoichi-dpo-v2-7... Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/NotAiLOL/Eclipse-mistral-4x7b

Eclipse Mistral 4x7b Parameters and Internals

LLM Name	Eclipse Mistral 4x7b
Repository 🤗	https://huggingface.co/NotAiLOL/Eclipse-mistral-4x7b
Base Model(s)	Kunoichi DPO V2 7B Hermes 2 Pro Mistral 7B MaziyarPanahi/Calme-7B-Instruct-v0.9 MetaMath Mistral 7B SanjiWatsuki/Kunoichi-DPO-v2-7B NousResearch/Hermes-2-Pro-Mistral-7B MaziyarPanahi/Calme-7B-Instruct-v0.9 meta-math/MetaMath-Mistral-7B
Model Size	24.2b
Required VRAM	48.5 GB
Updated	2024-08-10
Maintainer	NotAiLOL
Model Type	mixtral
Instruction-Based	Yes
Model Files	4.9 GB: 1-of-10 5.0 GB: 2-of-10 5.0 GB: 3-of-10 4.9 GB: 4-of-10 5.0 GB: 5-of-10 5.0 GB: 6-of-10 5.0 GB: 7-of-10 5.0 GB: 8-of-10 5.0 GB: 9-of-10 3.7 GB: 10-of-10
Model Architecture	MixtralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.40.2
Tokenizer Class	LlamaTokenizer
Padding Token	<s>
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the Eclipse Mistral 4x7b 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 Eclipse Mistral 4x7b

Best Alternatives	Context / RAM	Downloads	Likes
Proto Athena 4x7B	32K / 48.4 GB	5	0
Proto Athena V0.2 4x7B	32K / 48.4 GB	8	0
...erges MoE 4x7b V10 Mixtralv0.3	32K / 48.3 GB	6	0
Sixtyoneeighty 4x7B V1	32K / 48.3 GB	62	0
MixtureofMerges MoE 4x7bRP V11	32K / 48.3 GB	4	1
West Ramen 7Bx4	32K / 48.7 GB	53	0
MoE Merging	32K / 48.3 GB	70	0
Boundary Mistral 4x7b MoE	32K / 48.7 GB	117	1
...tral Fusion 4x7B Instruct V0.1	32K / 48.5 GB	8	18
... Extraction 4x7B Instruct V0.1	32K / 48.5 GB	4	5

Note: green Score (e.g. "73.2") means that the model is better than NotAiLOL/Eclipse-mistral-4x7b.