What are the hardware requirements for Wolfram Miqu 1 120B 2.4bpw H6 EXL2?

Wolfram Miqu 1 120B 2.4bpw H6 EXL2 requires approximately 36.7 GB of VRAM and supports a context window of 31K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Wolfram Miqu 1 120B 2.4bpw H6 EXL2 and how large is it?

Wolfram Miqu 1 120B 2.4bpw H6 EXL2 is developed by LoneStriker, a model with 70b 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 Wolfram Miqu 1 120B 2.4bpw H6 EXL2?

Wolfram Miqu 1 120B 2.4bpw H6 EXL2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Wolfram Miqu 1 120B 2.4bpw H6 EXL2 by LoneStriker — VRAM 36.7GB, 31K context

Name: Wolfram Miqu 1 120B 2.4bpw H6 EXL2
Author: LoneStriker

Wolfram Miqu 1 120B 2.4bpw H6 EXL2 is an open-source language model by LoneStriker. Features: 70b LLM, VRAM: 36.7GB, Context: 31K, Quantized, LLM Explorer Score: 0.11.

Base model:152334h/miqu-1-70b-... Base model:finetune:152334h/mi... Conversational De En Endpoints compatible Es Exl2 Fr It Llama Merge Mergekit Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-2.4bpw-h6-exl2

Wolfram Miqu 1 120B 2.4bpw H6 EXL2 Parameters and Internals

Additional Notes

Includes various EXL2 and GGUF quantization models.

Supported Languages

en (proficient), de (proficient), fr (proficient), es (proficient), it (proficient)

Training Details

Methodology:

created by interleaving layers of models using mergekit

Context Length:

32764

Model Architecture:

Frankenmerge of miqu-1-70b

Input Output

Input Format:

Mistral ~~[INST] {prompt} [/INST]

LLM Name	Wolfram Miqu 1 120B 2.4bpw H6 EXL2
Repository 🤗	https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-2.4bpw-h6-exl2
Base Model(s)	Miqu 1 70B Sf 152334H/miqu-1-70b-sf
Model Size	70b
Required VRAM	36.7 GB
Updated	2026-07-14
Maintainer	LoneStriker
Model Type	llama
Model Files	8.6 GB: 1-of-5 8.6 GB: 2-of-5 8.6 GB: 3-of-5 8.5 GB: 4-of-5 2.4 GB: 5-of-5
Supported Languages	en de fr es it
Quantization Type	exl2
Model Architecture	LlamaForCausalLM
Context Length	32764
Model Max Length	32764
Transformers Version	4.37.2
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	32000
Torch Data Type	float16

Rank the Wolfram Miqu 1 120B 2.4bpw H6 EXL2 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 Wolfram Miqu 1 120B 2.4bpw H6 EXL2

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...B Instruct Gradient 1048K 8bit	1024K / 75 GB	41	5
...B Instruct Gradient 1048K 4bit	1024K / 39.7 GB	25	3
...B Instruct Gradient 1048K 2bit	1024K / 21.9 GB	25	2
...0B Instruct Gradient 262K 4bit	256K / 39.7 GB	25	3
...0B Instruct Gradient 262K 8bit	256K / 75 GB	26	2
...0B Instruct Gradient 262K 2bit	256K / 21.9 GB	20	1
... Gradient 262K 2.25bpw H6 EXL2	256K / 22.2 GB	8	0
...t Gradient 262K 4.0bpw H6 EXL2	256K / 37.2 GB	7	1
...t Gradient 262K 2.4bpw H6 EXL2	256K / 23.5 GB	6	0
...t Gradient 262K 3.5bpw H6 EXL2	256K / 32.9 GB	6	0

Note: green Score (e.g. "73.2") means that the model is better than LoneStriker/wolfram_miqu-1-120b-2.4bpw-h6-exl2.