What are the hardware requirements for Casperhansen Mixtral Instruct AWQ Clone Dec23?

Casperhansen Mixtral Instruct AWQ Clone Dec23 requires approximately 24.7 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 Casperhansen Mixtral Instruct AWQ Clone Dec23 and how large is it?

Casperhansen Mixtral Instruct AWQ Clone Dec23 is developed by JAdeojo, a model with 46.7b 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 Casperhansen Mixtral Instruct AWQ Clone Dec23?

Casperhansen Mixtral Instruct AWQ Clone Dec23 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Casperhansen Mixtral Instruct AWQ Clone Dec23 by JAdeojo — VRAM 24.7GB, 32K context

Name: Casperhansen Mixtral Instruct AWQ Clone Dec23
Author: JAdeojo

Casperhansen Mixtral Instruct AWQ Clone Dec23 is an open-source language model by JAdeojo. Features: 46.7b LLM, VRAM: 24.7GB, Context: 32K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.11.

4-bit Awq Conversational Endpoints compatible Instruct Mixtral Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/JAdeojo/casperhansen-mixtral-instruct-awq-clone-dec23

Casperhansen Mixtral Instruct AWQ Clone Dec23 Parameters and Internals

LLM Name	Casperhansen Mixtral Instruct AWQ Clone Dec23
Repository 🤗	https://huggingface.co/JAdeojo/casperhansen-mixtral-instruct-awq-clone-dec23
Model Size	46.7b
Required VRAM	24.7 GB
Updated	2026-05-17
Maintainer	JAdeojo
Model Type	mixtral
Instruction-Based	Yes
Model Files	10.0 GB: 1-of-3 10.0 GB: 2-of-3 4.7 GB: 3-of-3
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	MixtralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.36.2
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	float16

Rank the Casperhansen Mixtral Instruct AWQ Clone Dec23 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 Casperhansen Mixtral Instruct AWQ Clone Dec23

Best Alternatives	Context / RAM	Downloads	Likes
Mixtral 8x7B Instruct V0.1 AWQ	32K / 24.7 GB	5	0
Dolphin 2.7 Mixtral 8x7b AWQ	32K / 24.7 GB	2550	23
Mixtral Instruct AWQ	32K / 24.7 GB	2333	43
Mixtral 8x7B Instruct V0.1 AWQ	32K / 24.7 GB	1045	59
Mixtral 8x7B Instruct V0.1 AWQ	32K / 24.7 GB	1248	16
...ixtral Instruct 8x7b Zloss AWQ	32K / 24.7 GB	0	2
...0.1 LimaRP ZLoss DARE TIES AWQ	32K / 24.7 GB	3	3
...Instruct V0.1 LimaRP ZLoss AWQ	32K / 24.7 GB	8	1
...utLM Mixtral 8x7B Instruct AWQ	32K / 24.7 GB	61	2
Dolphin 2.6 Mixtral 8x7b AWQ	32K / 24.7 GB	32	12

Note: green Score (e.g. "73.2") means that the model is better than JAdeojo/casperhansen-mixtral-instruct-awq-clone-dec23.