What are the hardware requirements for Jett W26 Abliterated?

Jett W26 Abliterated 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 Jett W26 Abliterated and how large is it?

Jett W26 Abliterated is developed by giannisan, 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 Jett W26 Abliterated?

Jett W26 Abliterated is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Jett W26 Abliterated by giannisan — VRAM 14.4GB, 32K context

Name: Jett W26 Abliterated
Author: giannisan

Jett W26 Abliterated is an open-source language model by giannisan. Features: 7b LLM, VRAM: 14.4GB, Context: 32K, Instruction-Based, Code Generating, LLM Explorer Score: 0.13.

Arxiv:2306.01708 Arxiv:2311.03099 Base model:chihoonlee10/t3q-mi... Base model:inferenceillusionis... Base model:kukedlc/jupiter-k-7... Base model:merge:chihoonlee10/... Base model:merge:inferenceillu... Base model:merge:kukedlc/jupit... Base model:merge:s-miguel/the-... Base model:merge:senseable/wes... Base model:merge:yam-peleg/exp... Base model:s-miguel/the-trinit... Base model:senseable/westlake-... Base model:yam-peleg/experimen... Codegen Endpoints compatible Instruct Merge Mergekit Mistral Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/giannisan/Jett-w26-abliterated

Jett W26 Abliterated Parameters and Internals

Model Type

pre-trained, language model

Additional Notes

This model is a merge of several pre-trained models using the mergekit tool.

Training Details

Methodology:

Merged using the DARE TIES merge method

LLM Name	Jett W26 Abliterated
Repository 🤗	https://huggingface.co/giannisan/Jett-w26-abliterated
Base Model(s)	T3Q Mistral Orca Math DPO WestLake 7B V2 The Trinity Coder 7B Experiment26 7B Excalibur 7B DPO Jupiter K 7B Slerp chihoonlee10/T3Q-Mistral-Orca-Math-DPO senseable/WestLake-7B-v2 S-miguel/The-Trinity-Coder-7B yam-peleg/Experiment26-7B InferenceIllusionist/Excalibur-7b-DPO Kukedlc/Jupiter-k-7B-slerp
Model Size	7b
Required VRAM	14.4 GB
Updated	2026-06-30
Maintainer	giannisan
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
Generates Code	Yes
Model Architecture	MistralForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.41.2
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the Jett W26 Abliterated 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 Jett W26 Abliterated

Best Alternatives	Context / RAM	Downloads	Likes
SpydazWeb AI HumanAI RP	512K / 14.4 GB	16	1
SpydazWeb AI HumanAI 002	512K / 14.4 GB	18	1
Lexi Coder V5.1	32K / 14.5 GB	197	0
Mistral 7B Merged	32K / 14.5 GB	6	0
Ultra Llm Merged	32K / 14.5 GB	7	0
...Mistral 7B Instruct V0.2 Slerp	32K / 14.5 GB	6	1
Damysus Coder V0.1	32K / 14.4 GB	17	0

Note: green Score (e.g. "73.2") means that the model is better than giannisan/Jett-w26-abliterated.