What are the hardware requirements for Xgen 7B 8K Open Instruct?

Xgen 7B 8K Open Instruct requires approximately 13.7 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Xgen 7B 8K Open Instruct and how large is it?

Xgen 7B 8K Open Instruct is developed by VMware, 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 Xgen 7B 8K Open Instruct?

Xgen 7B 8K Open Instruct is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Xgen 7B 8K Open Instruct by VMware — VRAM 13.7GB, 8K context

Name: Xgen 7B 8K Open Instruct
Author: VMware

Xgen 7B 8K Open Instruct is an open-source language model by VMware. Features: 7b LLM, VRAM: 13.7GB, Context: 8K, License: cc-by-3.0, Instruction-Based, LLM Explorer Score: 0.07.

Dataset:vmware/open-instruct En Endpoints compatible Ext 8k Instruct Llama Pytorch Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/VMware/xgen-7b-8k-open-instruct

Xgen 7B 8K Open Instruct Parameters and Internals

Model Type

text-generation

Additional Notes

Explicit note to set trust_remote_code=True when using the tokenizer. Contributions with finetuning scripts in RAIL Github Repository but evaluation TODO.

Supported Languages

en (high)

Training Details

Data Sources:

VMware/open-instruct, Mosaic/Dolly-HHRLHF, filtered OASST1, Subset of COT SUBMIX (FROM FLAN V2) Zeroshot examples

Methodology:

The model is an instruction-tuned version using the Alpaca prompt template, expanded with commercially viable zero-shot COT datasets from Flan v2 to total of 140k instruct-prompt responses. Supports prompt input of up to 8192 tokens.

Context Length:

8192

Input Output

Input Format:

Text prompt template with Instruction

Accepted Modalities:

text

Output Format:

Generated text based on instruction prompt

Performance Tips:

Ensure tiktoken library is installed and trust_remote_code is set to True for tokenizer. Use cuda for token processing.

LLM Name	Xgen 7B 8K Open Instruct
Repository 🤗	https://huggingface.co/VMware/xgen-7b-8k-open-instruct
Model Size	7b
Required VRAM	13.7 GB
Updated	2026-07-21
Maintainer	VMware
Model Type	llama
Instruction-Based	Yes
Model Files	9.9 GB: 1-of-2 3.8 GB: 2-of-2 9.9 GB: 1-of-2 3.8 GB: 2-of-2
Supported Languages	en
Context Length	8k
Model Architecture	LlamaForCausalLM
License	cc-by-3.0
Context Length	8192
Model Max Length	8192
Transformers Version	4.30.2
Tokenizer Class	XgenTokenizer
End of Sentence Token	<\|endoftext\|>
Vocabulary Size	51200
Torch Data Type	bfloat16

Rank the Xgen 7B 8K Open Instruct 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

Quantized Models of the Xgen 7B 8K Open Instruct

Model	Likes	Downloads	VRAM
Xgen 7B 8K Open Instruct Gptq	4	5	4 GB

Best Alternatives to Xgen 7B 8K Open Instruct

Best Alternatives	Context / RAM	Downloads	Likes
124	1024K / 16.1 GB	93	0
A5.4	1024K / 16.1 GB	12	0
A3.4	1024K / 16.1 GB	13	0
A2.4	1024K / 16.1 GB	12	0
... Qwen2.5llamaify 7B V23.1 200K	195K / 15.2 GB	29	5
Salamandra 7B Instruct 2606	160K / 15.6 GB	815	3
SuperNeuralDreadDevil 8B	128K / 16.1 GB	28	1
Falcon3 7B Instruct	32K / 14.8 GB	11749	80
Falcon3 Jessi V0.4 7B Slerp	32K / 14.9 GB	34	9
Jessi V0.4 Falcon3 7B Instruct	32K / 14.8 GB	22	0

Note: green Score (e.g. "73.2") means that the model is better than VMware/xgen-7b-8k-open-instruct.