What are the hardware requirements for CollAIborate4x7B?

CollAIborate4x7B requires approximately 48.7 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 CollAIborate4x7B and how large is it?

CollAIborate4x7B is developed by collaiborateorg, a model with 7b 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 CollAIborate4x7B?

CollAIborate4x7B is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

CollAIborate4x7B by collaiborateorg — VRAM 48.7GB, 32K context

Name: CollAIborate4x7B
Author: collaiborateorg

CollAIborate4x7B is an open-source language model by collaiborateorg. Features: 7b LLM, VRAM: 48.7GB, Context: 32K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.11.

Conversational Endpoints compatible Instruct Mixtral Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/collaiborate-tech/CollAIborate4x7B

CollAIborate4x7B Parameters and Internals

LLM Name	CollAIborate4x7B
Repository 🤗	https://huggingface.co/collaiborate-tech/CollAIborate4x7B
Model Size	7b
Required VRAM	48.7 GB
Updated	2026-06-08
Maintainer	collaiborateorg
Model Type	mixtral
Instruction-Based	Yes
Model Files	1.9 GB: 1-of-25 1.9 GB: 2-of-25 2.0 GB: 3-of-25 2.0 GB: 4-of-25 1.9 GB: 5-of-25 2.0 GB: 6-of-25 2.0 GB: 7-of-25 1.9 GB: 8-of-25 2.0 GB: 9-of-25 2.0 GB: 10-of-25 2.0 GB: 11-of-25 1.9 GB: 12-of-25 2.0 GB: 13-of-25 2.0 GB: 14-of-25 1.9 GB: 15-of-25 2.0 GB: 16-of-25 2.0 GB: 17-of-25 1.9 GB: 18-of-25 2.0 GB: 19-of-25 2.0 GB: 20-of-25 1.9 GB: 21-of-25 2.0 GB: 22-of-25 2.0 GB: 23-of-25 2.0 GB: 24-of-25 1.5 GB: 25-of-25
Model Architecture	MixtralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.38.1
Tokenizer Class	LlamaTokenizer
Padding Token	<s>
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the CollAIborate4x7B 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 CollAIborate4x7B

Best Alternatives	Context / RAM	Downloads	Likes
Mini Mixtral V0.2	32K / 25.8 GB	3923	4
OpenMistral MoE	32K / 48.3 GB	1218	0
Multilingual Mistral	32K / 93.5 GB	65	2
Magiq 3	32K / 37.1 GB	5	3
FNCARL9000	32K / 48.3 GB	8	0
Bigstral 12B 32K 8xMoE	32K / 163.3 GB	12	2
CollAIborate4x7B	32K / 48.7 GB	1	1
OpenMistral MoE	32K / 48.3 GB	6	0
...t V0.2 2x7B MoE 6.0bpw H6 EXL2	32K / 9.9 GB	3	1

Note: green Score (e.g. "73.2") means that the model is better than collaiborate-tech/CollAIborate4x7B.